In today's rapidly evolving technological landscape, mastering the Well-Architected Framework is not just crucial—it's the compass guiding businesses toward resilient, high-performing, and efficient cloud solutions.

A large number of businesses in recent years have shifted towards a cloud environment. But the question is does mere adoption solve all the problems? No, adoption does not guarantee cost-effectiveness and operational efficiency.

This is where a well-architected framework steps in to fill the gap. It was developed by Amazon Web Services (AWS), a leading cloud computing platform. It's a set of practices designed to help businesses implement secure, reliable, cost-effective, and efficient cloud architecture.

A periodic review of your cloud architecture and framework is crucial to ensure your cloud solution meets the highest standard of security, reliability, and efficiency. In this article, we'll explore the world of well-architected framework review, exploring benefits and significance. Businesses can maximize their cloud investment by implementing best practices and identifying areas for improvement.

Let's dive into the article. We'll start by understanding the pillars of a well-architected framework.

Understanding Key Pillars of Well-Architected Framework#

A well-architected framework is crucial for creating applications and infrastructure in the cloud. The framework is built around 5 key pillars. Each pillar addresses the critical aspect of resilient, efficient, and robust architecture that aligns with business goals.

Security: Security is an essential pillar of the framework. There is always a risk of cyber-attacks and data breaches. The security pillar emphasizes the implementation of access and identity controls, and encryption. Security is vital to ensure data integrity and confidentiality throughout an application's lifecycle.

Reliability: Reliability is another pillar of a well-architected framework. This pillar emphasizes the design of the application, the application should be able to recover from failures instantly. It significantly affects the user experience. By leveraging scaling and fault tolerance organizations can ensure high availability and minimal downtime, boosting the customer experience.

Performance Efficiency: Performance is another essential pillar of the framework. By monitoring and ensuring reliability organizations can increase the response time and efficiency of the application deployment process. By incorporating best practices based on the data available, organizations can cost-optimize and provision workloads effectively.

Cost Optimization: Cost optimization while maintaining high quality is a challenge. The cost optimization pillar guides businesses to identify cost drivers and leverage cloud-native applications to maintain the desired level of application. By analyzing patterns organizations can

Operational Excellence: The operational excellence pillar of the framework enhances operations through best practices and strategies. These practices include automation, continuous improvement, and streamlined management.

The Well-Architected Framework Review involves assessing an architecture against these five pillars. AWS provides a set of questions and considerations for each post, allowing organizations to evaluate their systems and identify areas for improvement.

Now let's review some components of a well-architected framework. We'll review the significance of each component. We'll also explore strategies and best practices.

Data Integrity Considerations in a Well-Architected Framework:#

Data Integrity is a crucial aspect of a well-architected framework in cloud optimization. In recent years the volume of cyber attacks on IT companies has skyrocketed. Organizations store sensitive data of users and other organizations. Data breaches not only affect the reputation of the organization but also put users at risk.

Because of sensitive user data, many industries have regulations for data integrity. So data breaches also open up an organization to legal consequences. Cyber attacks also affect the operational and decision-making capacity of an organization.

To ensure data integrity organizations can utilize encryption, access control, identity management, and Backup and recovery features.

Encryption helps protect both data at rest and in transit. Strong encryption is crucial for data protection even in case of disaster.

Strong IAM ( Identity and Access Management) is also vital for data integrity, access should be granted based on assigned roles.

Cost Optimization in a Well-Architected Framework#

Cost optimization is a critical aspect of cloud architecture, especially in the financial services industry. Financial services industry workloads are different and cost challenges are unique. In other industries, cost optimization is relatively easy.

The finance industry, however, has a lot of strings attached, such as regulatory compliance, sensitive user data, and demanding workloads. This section explores practices to ensure cost optimization.

Financial services industry workloads are data intensive and require real-time processing. This need for storage and processing power can increase cloud costs significantly if not managed properly.

For cost optimization analyze workloads and provision resources accordingly. For significant cost reduction, you can utilize automating scaling and spot instances. Automating scaling automatically adjusts the resources according to demand whereas spot instances allow you to use spare computer capacity at a fraction of on-demand price.

Release Management: Seamless Deployment and Change Control#

Release management is a crucial aspect of cloud architecture. Release management ensures new features, updates, and bug fixes reach the end user quickly and the application works smoothly. It is the pillar of the Well-Architected Framework that ensures smooth software deployment and version control.

Effective release management strategies include automation, version control, and seamless release cycles. Implementing automated testing ensures code is of high quality and bugs and other errors are caught early in the development stage. Automation in software development ensures the development lifecycle becomes efficient and the chances of human error are reduced.

Version control is an essential consideration for seamless deployment. Version control stores code history from the start of development. Version control ensures errors are identified and fixed quickly. Branching is another helpful strategy, you can utilize it to work on new features without affecting the code. Prepare rollback plans in case of failed deployments.

Release management practices in a well-architected framework offer several benefits including consistency, flexibility, reduced risks, and faster time to market.

Monitoring Performance and Ensuring Reliability:#

Performance and Reliability are crucial in a cloud architecture. Performance and reliability directly impact the user experience. The monitoring performance and reliability pillar within a well-architected framework emphasizes real-time monitoring and proactive cloud optimization.

Monitoring performance in real-time is crucial to ensure the proper functioning of the application. Monitoring performance helps identify and resolve problems early on. Another benefit of real-time monitoring is you can identify and remove security bottlenecks.

Monitor key metrics and design a sustainable cloud architecture. Design mechanism for automated recovery and automated scaling. Use load-balancing techniques for better sustainability. Also, design your cloud architecture with a failover mechanism for high availability.

Monitoring performance and reliability practices offer several benefits which include proactive capacity planning, resilience, and timely issue resolution.

Sustainability and Scalability in Architecting Workloads:#

In a well-architected framework, the sustainability pillar is about managing the workload in a way that not only meets the current needs but also prepares for future demands. For better sustainability and scalability architect workloads to make optimal use of resources.

Some successful strategies for scalability and sustainability are autoscaling and serverless architecture. Auto-scaling automatically scales up and down resources according to demand. Utilize serverless architecture to automatically scale applications without the need for physical servers.

For long-term growth use microservices application architecture where each component works independently. Use cloud models that best match your long-term plans. Architect designs to accommodate new technologies and stay up to date.

Managing Containerized Workloads in the Framework Review:#

Containerization is a revolutionary approach to application development. It enhances agility, scalability, and reliability within the cloud environment. Managing Containerized workloads within a well-architected framework focuses on optimizing applications with the help of container technology. A popular technology for managing containerized workloads is Docker and a popular orchestration tool is Kubernetes.

Containers provide an environment where an application can be placed with its dependencies to ensure consistency. Managing containerized workloads helps scale applications efficiently.

One of the popular orchestration tools is Kubernetes. It automates the development life cycle and management of applications. Implement best practices for required results. Scan images for vulnerabilities, monitor resources to ensure proper provision, and utilize automation for best results.

Implementing Containerization and orchestration within a well-architected framework aligns with Performance Efficiency, Reliability, and Operational Excellence.

Serverless Applications for Operational Efficiency:#

Serverless application architecture within a well-architected framework focuses on operational efficiency, cost-effectiveness, and scalability. In recent years serverless architecture has wholly revolutionized the software development landscape. Organizations are focused on the build, test, and deployment lifecycle of code rather than the underlying infrastructure.

Serverless architecture provides real-time processing power and is suitable for event-driven applications such as transaction and report generation etc. The best use case of serverless applications is financial services industry workloads., where real-time processing is required all the time.

A combination of serverless applications and monitoring tools can provide cost optimization, scalability, and efficiency. Organizations can achieve operational excellence and efficiency by implementing serverless applications.

Nife Labs: Revolutionizing Cloud Solutions with a Hybrid Approach#

Introducing Nife Labs a hybrid cloud computing platform that helps businesses navigate through complex modern cloud computing.

Nife Labs bridges gaps in cloud architecture, aligning with the Well-Architected Framework's principles.

Nife ensures data security through encryption and efficient key management. It offers pricing options suited for varied workloads. It streamlines development facilitating agile and reliable releases.

Elevate Your Cloud Experience with Nife Labs. Explore Now!

Conclusion:#

In conclusion, the Well-Architected Framework acts as a guide to organizations seeking cloud optimization. From data integrity and cost optimization to release management and cutting-edge practices like serverless computing, its pillars provide a roadmap to success. For the Financial Services Industry workloads, these practices ensure security and scalability. By adhering to this framework, businesses forge adaptable, efficient, and secure pathways to navigate the complexities of modern cloud computing.

Computer vision solutions are becoming a very important part of our daily life. It has many valuable applications in various fields from facial recognition to self-driving vehicles and medical imaging. It has applications everywhere. It allows machines to analyze images and identify people and objects with great accuracy and precision.

No doubt the technology is very powerful, but its capabilities are being limited by traditional cloud infrastructure. This is where cloud edge computing steps in. Cloud edge computing has provided the necessary speed and infrastructure to utilize computer vision applications at their best.

The importance of Cloud edge computing in providing efficient deployment of computer vision applications can not be overstated. Cloud Edge infrastructure processes the data of users at the edge of the network, where it is being generated. It provides low latency and real-time processing power, making it ideal for various computer vision applications.

In this article, we will explore the challenges as well as strategies for efficiently deploying computer vision solutions with edge computing. Read the full article for complete insights.

Computer Vision and Edge Computing#

Before jumping into the topic let's explore cloud vision technology and edge computing in detail.

What is Computer Vision?#

Computer Vision is a field of AI (Artificial Intelligence) that enables machines to interpret and analyze visual data (images and videos) intelligently. It uses different algorithms, machine learning, and deep neural networks for that.

In the last few years, it has improved very much in capabilities. It has various applications in different fields. Some computer vision applications are facial recognition, object detection, and self-driving vehicles.

What is Edge Computing?#

Edge computing is a type of cloud computing that uses IoT devices to process data closer to the source of its generation. It provides many benefits including low latency, high bandwidth, high speed, reliability, and security. It reduces the dependence on a centralized cloud solution.

Relationship#

Computer vision applications need to process large amounts of data. Edge computing enables the processing of a large amount of visual data in real-time. Which allows machines to make informed decisions at a higher speed.

Their relationship can significantly improve different fields including manufacturing, retail, healthcare, and more.

Challenges in Deploying Computer Vision Solutions with Edge Computing#

The advantages of deploying computer vision solutions with edge computing can not be denied. But there are also some challenges and concerns that need to be addressed. These challenges include security and privacy concerns, power constraints, latency and bandwidth issues, and security.

Latency and Bandwidth Issues#

One of the important challenges in the deployment of computer vision solutions with edge computing is latency and bandwidth issues. Data is processed at the edge of the network, close to the source in edge computing. The processing capabilities of edge devices are limited and computer vision applications usually require a large amount of processing power.

This may increase the latency of the speed and affect the real-time decision-making capabilities. However, this problem can be resolved by selectively sending data to the cloud for low latency.

Security and Privacy Concerns#

Edge computing infrastructure involves the deployment of multiple connected devices. These devices are deployed in an unsafe environment and are always vulnerable to cyber attacks. Important data collected by these devices can be compromised. These security and privacy concerns can be addressed by using encryptions and access controls.

Power Constraints#

Edge devices usually have limited battery capacities. These batteries can dry up pretty quickly during the processing of vast amounts of data. In that case, it can create operational challenges. It is important to take necessary actions to avoid these types of problems.

Scalability#

Another big challenge in the deployment of computer vision applications is scalability. As processing requirements of computer vision applications are huge. To fulfill these processing requirements, a large number of edge devices are required. It can be difficult to manage these large numbers of devices which can eventually create scalability challenges.

Strategies for Efficient Deployment of Computer Vision Solutions with Edge Computing#

Efficient deployment of computer vision solutions with edge computing can be done by implementing some useful strategies. Here are some of the strategies that can be used to improve efficiency.

Edge Device Selection#

Choosing edge devices is a very important strategy in deploying computer vision solutions. Edge devices need to be selected based on capabilities such as processing power, battery, memory, connectivity, and reliability. Computer vision deployment requires the processing of vast amounts and latency for real-time decision-making. That is why it is crucial to select devices carefully.

Machine Learning Models and Algorithms#

Machine learning models and algorithms play a crucial role in the efficient deployment of computer vision solutions. Edge devices are not capable of processing these language models and algorithms. Therefore lightweight language models and algorithms can be used for speed and accuracy. These lightweight models deliver without compromising quality.

Cloud Edge Hybrid Solutions#

Another important strategy for the deployment of Computer Vision solutions with edge computing is the use of hybrid solutions. Computer vision applications require large storage and processing power. By implementing hybrid solutions these needs can be addressed efficiently. Organizations can use cloud resources for important data while day-to-day processing edge devices can be used. Hybrid infrastructure provides security, reliability, and speed.

Use Cases:#

Here are some of the applications of efficient deployment of computer vision solutions with edge computing.

Smart Cities and Traffic Management#

Computer vision combined with edge computing can be used in smart cities for surveillance and traffic management. Edge camera devices with censors utilizing computer vision algorithms can be used to control traffic flow. These devices can analyze real-time data and adjust traffic effectively by making informed decisions. In this way, accidents can be avoided and a proper traffic flow can be maintained.

Healthcare#

Another important application of computer vision and edge computing is healthcare. Edge devices enable remote diagnosis of patients. Edge devices with sensors allow patients to detect diabetes, heart diseases, and respiratory illnesses from their homes. These are some diseases that need regular checkups. Edge devices allow patients to transfer their medical history to their hospitals. Moreover, edge devices also allow patients to consult doctors from their homes using cameras and get their diagnosis.

Manufacturing#

Efficient deployment of computer vision solutions with edge computing can be used to improve the efficiency of manufacturing plants. Edge devices with computer vision technology can be used to monitor product lines, inventory, and manufacturing processes. Edge devices can be used to make real-time adjustments in the manufacturing process.

Agriculture#

Another important application of computer vision with edge computers is agriculture. Edge devices with computer vision technology can provide many benefits to farmers. These devices can automatically detect water levels in crops and give water whenever required. These devices are also capable of detecting pesticides and diseases in crops.

There are many more applications of edge computing and computer vision in agriculture fields. With proper deployment, these applications can provide many benefits to farmers.

Conclusion:#

Efficient deployment of computer vision solutions with edge computing can provide many benefits in different industries, from healthcare and automotive to manufacturing and agriculture.

Edge computing combined with computer vision allows room for efficiency, accuracy, scalability, and cost-effective solutions.

There are some challenges associated with the technology which can be addressed through proper planning. Overall the potential of edge computing and computer vision is limitless. With more innovations in the field, the applications are expected to grow.

Introduction#

The multi-cloud management is an innovative solution to increase business effectiveness. Because of the custom-made IT solutions on multi-cloud used by businesses for rapid deployments, it results in greater profitability. The use of multi-cloud by large and medium size organizations is based on the advantages offered by cloud computing. The competitive edge to select from the best cloud solution provider is a unique tool for business growth. The global organizations with maximum workloads gets benefitted from multi-cloud operations. The multi-cloud management offers uniqueness to business organizations and makes their operations reliable and safe. However, a business organization can also get negative impacts from technology. There are pros and cons of multi-cloud computing for organizations moving to multi-cloud infrastructure from private cloud services.

Multi-cloud Migration Pros and Cons#

Businesses always migrate from one technolgical platform to other searching profitability. Cloud based migration is enabling businesses to open up to innovative solutions. Currently, there is an on-demand scope of migrating to multi-cloud architecture. The aim is to get benefitted from the pile of IT solutions available from across the best on the cloud. Businesses are carefully selecting the most competitive cloud management considering pros and cons simultanesouly.

Benefits of Migrating to Multi-Cloud Solutions#

There are various benefits that organizations can drive from multi-cloud management elaborated below:

Rapid Innovation#

• Modern businesses migrating to multi-cloud deployment are seeking innovation at a rapid pace that results in changing branding and scalability.
• The use of multi-cloud management offers limitless solutions to business that improves customer approachability.
• Best outcomes from the selection of best services on multi-cloud gives freedom to choose from the very best.

Risk Mitigation#

• Using the multi-cloud infrastructure the businesses are given a risk-free workability that is generated through an independent copy of the application on the cloud server.
• The use of multi-cloud deployment in case of any disruption ensures that businesses on the multi-cloud computing management are working continuously.

Avoiding Vendor Lock-In#

• This is one of the greater benefits to organizations moving their business onto multi-cloud computing management. The private and public cloud services offer restricted access to the services and capabilities.
• Hence, businesses using public or private cloud services offer a lock-In that does not generate competitiveness of the services. Thus, multi-cloud management and multi-cloud providers effectively render opportunities that enable the business to switch services reducing its dependency.

Lower Latency#

• The use of multi-cloud computing is effective in transferring data from one application to another. Migration of the business to a multi-cloud management platform offers lower latency that enables the application and services to transfer their data at a rapid pace.
• This is directly connected with the application usage and its effectiveness for the user and is an advantage to the business migrating to the multi-cloud service.

Drawbacks of Migrating to Multi-Cloud Solutions#

The following are the drawbacks that businesses had to look into when migrating to the multi-cloud management platform:

Talent Management#

• with the growing conversion of business into multi-cloud computing platforms, organizations are struggling to find the right talent to operate and function effectively on the cloud systems.
• The decision to move to multi-cloud management requires skilled people who know how to work on cloud computing systems. With the increased pace of migration to multi-cloud, there is a shortage in the market for the right talent.

Increased Complexity#

• Adding a multi-cloud management platform into the business results in taking in services from the multi vendors as a part of risk mitigation, but it also adds complexity to the business.

• Handling various operational frameworks of software used by various vendors requires knowledge and training, a level of transparency, and technical know-how.

• The cost of managing a multi-talent team comes at accost along with managing the licensing, compliance, and security of the data.

• Thus, businesses migrating to multi-cloud management need to prepare a comprehensive cloud handling strategy to restrict the operational and financial dead-load.

Security Issues#

• The bitter truth is that realizes migrating to a multi-cloud management platform system is an increased risk to data safety.
• Multi-cloud services are provided by various vendors and thus create a vulnerability of IT risks.
• There is a regular issue of access control and ID verification as reported by users.
• Thus, a multi-cloud infrastructure is more difficult to handle as compared to a private cloud.
• Encryption keys and resource policies, requires multi-layer security because of different vendor accessibility.

It is evident that the use of multi-cloud infrastructure to innovate and grow the business has resulted in large-scale migration of businesses and companies across the globe. Post-pandemic work culture and business strategies also place migrating to multi-cloud as a part of future sustainability. Subsequently, there are issues in migrating to multi-cloud management and seeking multi-cloud services from various vendors. The advantages such as risk mitigation, rapid innovation, and avoiding vendor lock-in are the biggest motivation for businesses to migrate to multi-cloud as compared to the high security risks and need for expertise and its associated cost to hire and retain the talent within an organization are some of the positives. Thus, the future belongs to the multi cloud as the benefit offered are more then negatives.

If your enterprise is looking for a way to save cloud budget, do check out this video!

Why is there a need for Cloud Computing?#

Cloud computing is getting famous as an alternative to physical storage. Various advantages enable business organizers to prefer cloud computing to other data servers and storage options. One of the most prominent reasons setting the global acceptance and upsurge in the use of cloud computing is cost-saving applications of cloud computing reducing the cost of hardware and software required at the consumer end. The versatility of cloud computing provides the option to workload data access online through the internet from anywhere in the world with restriction of access timing. The innovation in cloud computing such as the integration of paying options, and switching over to applications in an easy manner highlights the growing need for cloud computing as a future solution to computing.

The effectiveness of cloud computing is linked to its massive use as a driver of transformation interlinking artificial intelligence, the Internet of Things (IoT) with remote and hybrid working. The involvement of metaverse, cloud-based gaming technologies, and even virtual and augmented reality (VR/AR). Using cloud computing enables users to avoid investing in buying or either owning an infrastructure that facilitates complex computing applications. Cloud computing is an example of “as-a-service” that makes running servers and data centers located miles apart like a connected ecosystem of technologies.

Multi-Cloud Market and its Trends in 2022 - 2023#

Early Trends#

The rise of cloud computing in the year 2020 and 2021 promises that market trends and acceptability to use multi-cloud computing will further increase. It was post-pandemic that the focus was on digital applications for conducting business within safety limits. With the development of new technologies and capabilities in cloud computing, every organization and business house is starting to get cloud computing integrated with daily business operations. Multi-cloud computing is a system of tools and process that helps organize, integrate, control, and manage the operations of more than one cloud service that were provided by more than one service vendor. As per the reports from Gartner, the predicted spending on the usage of multi-cloud services has reached \$482.155 billion in the year 2022 which is 20% more spending than in 2020.

Innovation Requirement#

The current market management of multi-cloud is segmented on the lines of deployment and market size. The strategic geographic location and demographic trends are also shaping the growth of multi-cloud use. Multi-cloud computing is resulting in increased usage of artificial intelligence (AI), and the internet of Things (IoT). Thus, further accelerating the use of remote and hybrid working as a new business culture. The role of multi-cloud is to be an enabler to move forward swiftly with the development of new technologies such as virtual and augmented reality (AR/VR), the metaverse, cloud-based virtual gaming, and leading quantum computing as well. By 2028, it is expected that the multi-cloud market will grow to become a multimillion-USD service industry.

Trends of Multi-cloud Computing in Asian Markets#

In the Asian region, the use multi-cloud market will increase because of greater workforce dependency on computing-related businesses. International Digitial Corporation (IDC) projected that in 2023, South Asian companies will generate 15% more revenue from digital products. A major bulk of this revenue will be based on growth and the emergence of multi-cloud services. Thus, one in every three companies will conduct business and earn 15% more while working on the cloud in 2023. In 2020, every one in six companies was getting benefitted from the cloud market. The existence of cloud computing knowledge is leading the upward trends in the Asian market.

Asian and African countries have traditionally been a place of physical connection rather than virtual ones. But, the pandemic of Covid-19 has changed that perception and the cultural stigma of going away to work. Governments of India, China, Hong Kong, Thailand, and Singapore are working towards taking their workloads on virtual cloud formats. Therefore, focusing on the future resilience of the work in case of the sudden emergence of any public health disaster. Thus, multi-cloud has become a prominent driver in changing the working process and methods of business. All organizations are developing contingency planning and emergency data recovery solutions. Multi-cloud provides recovery opportunities by storing the data on separate cloud providers.

The emergence and growth of multi-cloud computing is the next revolution in the IT world. The post-pandemic trends reflect greater demand for resilient infrastructure to safeguard businesses from global calamities in the near future. Therefore, Asian and South Asian countries are taking up multi-cloud computing as an alternative to private cloud services. Small and medium organizations in Asian countries are also taking up advantage of multi-cloud computing to improve their business prospects.

Edge Computing is booming all around the globe, so let us look in to what the latest Edge Computing Market trends in Asia are.

What is Edge Computing?#

The world of computing has been changing inter-dimensions venturing into new models and platforms. It is one such innovation that is an emerging concept of interconnected networks and devices which are nearby of one another. Edge computing results in greater processing speeds, with greater volumes to be shared among each user which also leads to real-time data processing. The model of edge computing has various benefits and advantages wherein the computing is conducted from a centralized data centre. With the growing knowledge about edge computing in organizations across the world, the trends are growing positively across all regions. The generation and growth of edge computing for enterprises in Asia is an incremental path with major countries' data consumers such as Singapore, China, Korea, India, and Japan looking to explore edge computing for IT-based benefits.

The emergence of the Asian Computing Market#

The development of the Asian computing market arises from the highest number of internet users in the countries like China, India, Singapore, Korea, and Japan. The development of the computing industry in small Asian countries such as Hong Kong, Malaysia, and Bangladesh has also created a demand for the adoption of global technologies like edge computing. These economies are converging towards digital currency and digital public services that aim to take advantage of edge computing. Asian emerging market is also undergoing rapid growth and transitioning into a technological industry base. The Philippines for example have been growing its internet user base with a 30% annual increment till 2025. Vietnam, another Asian country with a growing economy is also aiming to become to fastest-growing internet economy in the next decade. The demand of domestic nature is resulting creation of computing for Enterprises in Asia that are bound to give intense challenges to multinational IT companies.

Critical Importance of Edge Computing to Emerging Asian Markets#

The business centered on edge computing is creating a network of the most efficient process of social media, IoT, virtual streaming video platforms, and online gaming platforms. Edge computing offers effective public services offered through smart cities and regions. The trends for edge computing in Asia are increasing to reach \$17.8 billion within the next 3 years till 2025. Edge computing is the next big innovation that generates decentralized computing activities in data centres and business call centres. Edge computing can be used by various business industries to support the market presence of Asian markets. Nife for example has been gaining a lot of traction as one of the best application deployment platforms in Singapore for the year 2022. It offers one of the best edge computing platforms in Asia with clients in Singapore and India.

The development of Multi-cloud platforms in Asia is contributed to the high-skill workforce engaged in computer engineering. Businesses focused on digital tools and techniques, technology-based cross-collaboration between countries such as Singapore and India in the field of digital health, smart cities, and IT-based infrastructure is an example of edge computing for enterprises in Asia which is taken up by other Asian countries as well. Using edge computing platforms Asian business organizations are preventing the bottlenecks in infrastructure and services owing to a large number of consumers. The example of a multi-cloud platform in Singapore is notable for the benefits it is providing to business organizations. Nife as an organization is helping enterprises to build future business models to provide stronger digital experiences with an extra layer of security. The models based on the edge computing platforms are rapidly scalable and have a global scaling factor that can save cost when taking business in off-shore new markets.

Key Influencing trends supporting Edge Computing Market#

Edge computing is regarded as the best application deployment platform in Singapore as per the survey performed by Gartner in 2022. Various reasons are driving the edge computing used for enterprises in Asia based on low-latency processes and the influx of big data. The use of IoT, Artificial Intelligence, and the adoption of 5G is fostering the development of multi-clouding platforms. There are key trends that are shaping the development and growth of edge computing in the Singapore/Asian market and are illustrated as follows:

• IoT growth: Edge computing facilities the sharing of data when IoT devices are interconnected creating more secure data sharing with faster speed. The use of IoT devices based on edge computing renders optimization in real-time actions.
• Partnerships and acquisitions: the application of multi-cloud computing ecosystems is still developing in Asia based on service providers to connect with networks, cloud and data centre providers and enterprising the IT and industrial applications.

Conclusion#

Edge computing development in Singapore/Asia is surfaced as the best application deployment platform. The progress of edge computing is changing business development in the Asian market. The trends of greater application in the Asian market are reflected based on the growing number of internet users which is probably the largest in the world, adoption of the digital economy as a new model of industrial and economic development by most Asian countries such as Hong Kong, Malaysia, Thailand, India, and China. Such factors are positively helping local Edge Computing Enterprises to grow and compete in the space of multi-cloud services against the best in the world.

You can also check out the latest trends in the Gaming industry here!

This blog will explain the scope and future of Smart Cities in Singapore and India.

The future of "smart" cities begins with people, not technology. Cities are becoming increasingly habitable and adaptive as they become innovative. We are just witnessing what smart city technology can achieve in the urban environment.

Introduction#

According to [Smart City Index (2022)],

"An urban setting that uses a wide range of technological applications and executions for the enhancement and benefit of its citizens and reduce the urban management gaps is defined as a Smart City."

Singapore was ranked the top smart city in the world, followed by Helsinki and Zurich. India, however, lost its position in the top 100 list for 2020. It could be a result of pandemic-induced halts and stringent lockdowns applications in Indian cities.

Smart cities in the post-pandemic era are more relevant. It is especially considering the opportunities provided by smart city solutions in the management and effective service delivery for healthcare, education, and city management [(Hassankhani et al., 2021)].

Smart City Solutions#

Rapid urbanization has necessitated the development of smart city solutions. According to [(Elfrink & Kirkland, 2012)], future smart cities will be the facilitators of speeding economic growth and smart city infrastructure.

Smart City solutions extend across various domains.

Smart Parking#

• There are three types of smart parking solutions for Smart Parking:
  • ZigBee sensor-based
  • Ultrasonic sensor-based
  • Wi-Fi camera-based

which caters to street parking, interior parking, and multi-level parking.

• The Car Parking Occupancy Detection and Management system employs field-mounted Wi-Fi-based cameras. It is an end-to-end solution that is durable, dependable, and cost-effective.

Smart Traffic Management#

The Smart Traffic Management system provides centralized traffic lights and sensors that govern traffic flow across the city in response to demand. It includes the following:

• The smart vehicle inspection system
• System of Junction Control
• System for Counting Vehicles
• Junction Control Unit (JCU)

Smart Lights#

The cost-efficient rays of light from an innovative, networked street lighting solution illuminate the effective way to a future of a Smart City.

Smart Street Lighting Characteristics or Solutions:

• Maintenance Planning
• ON / OFF autonomy
• Grid Monitoring, Optimization, and Reporting
• Integrations of Smart City Platforms
• Installations are quick and inexpensive
• Communication Technology Agnostic

Smart Governance#

Smart Governance combines smart city technology with creative approaches to improve government service delivery and citizen participation in policy development and implementation [(Tan & Taeihagh, 2020)]. This approach, when used successfully, enables responsive, transparent, and inclusive policy decisions.

Smart City Solutions for e-Governance:

• The Citizen Portal
• Residential Data Hub for the State
• Monitoring of Service Desk Infrastructure
• Billing Administration
• E-Procurement
• Project administration
• Facility administration
• Election Details
• Monitoring of the road
• Management of Encroachment
• Job administration
• Monitoring of Parking Meters
• Fleet administration
• Monitoring of Road Sweeping
• Dashboard for City Performance

Smart Cities in India#

Smart city projects in India have been going on since 2014. Solutions include using digital mobile applications to provide beneficial services to everyday citizens, digital payment systems for government facilities and non-government services, and incorporating digital databases to manage the negative effects of urbanization.

Indian IoT smart cities like Delhi and Mumbai are facing uncontrolled urbanization and a crunch in public infrastructure. The government of India sees a possibility of developing IoT smart cities, effectively providing the roadmap for other cities to join the bandwagon as a part of growing smart cities in India. However, infrastructure is a primary requirement for growth.

The use of smart city technology in producing clean energy, clean energy consumption, and waste management requires the development of smart city infrastructure.

Smart Cities in Singapore#

Singapore is ranked 1st on the list of smart cities in the world. Smart city infrastructure, such as the decentralization of wastewater to produce clean potable water, smart cities in Singapore's infrastructure to create clean transportation, and enabling the participation of citizens in regular city management. These make Singapore a model for smart city solutions.

The innovative solution made for the smart city has enabled Singapore to become a nation that has always been among the top three countries leading smart cities in the world. Technology transfer, especially in sustainable transport, will be a hallmark of technology-driven collaboration between India and Singapore.

Singapore uses this infrastructure for sustainable transport that relies heavily on digitalization, making Singapore an IoT smart city.

Conclusion#

The potential of IoT is limitless. Urban data platforms, big data, and artificial intelligence can convert our urban centers into smart, sustainable, and efficient environments with large-scale implementation, deliberate deployment, and careful management.

The shared use of information is the key to the success of all industries, from healthcare to manufacturing and transportation to education. Our next-generation smart cities will be more innovative than ever by collecting data and implementing real solutions.

Let's Understand How DevOps vs. Agile vs. Traditional IT Works!#

DevOps vs Agile is a topic everyone is talking about.

When we consider the Software Delivery Life Cycle (SDLC), we frequently think of the most prevalent areas where content is created. Initially, software development did not fall under a single management umbrella. Software development may be described by the time it took to design or create an application.

In this blog, we will discuss the fundamental differences between conventional IT and Agile, as well as traditional IT against DevOps, to explain why DevOps has grown in popularity in recent years.

DevOps vs. Agile#

Given their resemblance, Agile and DevOps aren't the same, and some argue that DevOps is superior to Agile. It is critical to go down to the nuts and bolts to decrease complexity.

Comparison#

• There is no disputing that both are software development approaches.
• Agile has been around for nearly 20 years, while DevOps as a service has only lately entered the scene.
• Agile and DevOps are concerned with rapid software development, and their philosophies are centred on how quickly software can be generated while causing no harm to the customer or business (Hemon et al., 2019).

Differences#

The distinction between the two is what occurs following development.

• DevOps and Agile both use software development, measurement, and deployment. However, true agility tends to stick around after these three phases. DevOps as a service, on the other hand, includes frequent operations. As a result, monitoring and software development is ongoing.
• Separate individuals are responsible for building, testing, and delivering software in an Agile environment. Engineering specialists are responsible for everything in DevOps practices, including service development, operations development, and so on.
• Agile is more consistent with lean and decreasing waste, and ideas such as Agile development finance and minimum viable product (MVP) are applicable.
• Instead of predicting measurements, Agile emphasizes and incorporates empiricism (adaptability, openness, and scrutiny).

Important Distinctions

DevOps vs. Traditional IT#

When conventional IT operations are compared to DevOps, it is evident how they vary and why the latter is increasingly being adopted by organizations globally. Some comparisons are shown below.

Time#

DevOps teams invest 33% more time than traditional IT operations teams refining technology against failures. DevOps teams also devote less time to administrative assistance because of increased automation, self-service capabilities, and assistance scripts. With all of this extra time, DevOps as a service can devote 33% more time to infrastructure improvement and 15% more time to self-improvement through extra training and instruction.

Speed and Data#

DevOps teams are often small, flexible, motivated by creativity, and focused on completing work quickly. One of the top five DevOps goals is agility. The data count for the feedback loop in traditional IT operations is limited to the service or application being worked on. It is impossible to remedy a downstream consequence that is not known or noticed. IT operations must pick up the pieces. That is why Cloud DevOps is more efficient in providing business apps, and the challenge for IT operations is to stay up with the speed of business.

Recuperation and Crunch Time#

Being prepared for the risk of failure is a crucial DevOps as a service approach. Continuous testing, alarms, monitoring, and feedback loops are implemented to allow DevOps teams to respond rapidly and efficiently. Traditional IT operations teams demand twice as much time as DevOps teams. The crucial components for a speedy recovery are automated deployments and customizable infrastructure.

Software Distribution#

DevOps teams require around 36.6 minutes to deliver software, whereas conventional IT ops teams require approximately 85.1 minutes. This means that DevOps teams deliver apps at a rate that is more than twice as fast as traditional IT operations teams.

Conclusion#

Ultimately, the aims of Agile and DevOps are the same: to increase the pace and quality of software development, and discussing one without the other makes little sense. Traditional IT teams demand more time for each operation than the other two.

Many teams have found Agile approaches to be quite beneficial, while others have struggled to reap the benefits of an Agile approach. This might be due to a variety of factors, including teams not completely understanding or applying Agile techniques appropriately. It is also possible that adding a Cloud DevOps strategy can assist firms who struggle with Agile to bridge the gaps and achieve the results they desired.

Direct DevOps, DevOps automation, and DevOps communities are hot topics for anyone aiming to excel in the DevOps field.

Communities are often founded on shared challenges and learning experiences, with each community offering different resources depending on collective needs.

Starting as a newbie in the DevOps sector can be daunting, but numerous platforms offer valuable learning resources and networking opportunities. We’ve compiled a list of top DevOps communities that cover various aspects of DevOps education and practice.

1. Microsoft Learn#

Microsoft Learn is a free, interactive platform offering hands-on training for developing skills related to Microsoft products and services like Microsoft Azure DevOps. The platform provides various modules, learning paths, and quizzes to help learners understand DevOps concepts, though it currently only supports text-based content.

2. Reddit#

Reddit hosts numerous subcommunities and discussions for developers, including /r/DevOps, /r/SysAdmin, /r/Puppet, /r/ITIL, and /r/Docker. These forums provide a mix of questions and answers on specific technologies and broader DevOps practices.

3. Google Developers Groups#

Google Developers Groups connect software developers with similar interests through events and hands-on workshops. This community covers a wide range of technical topics and offers opportunities to learn about Google Cloud DevOps through both online and in-person events.

4. LinkedIn#

LinkedIn features various sub-communities where you can engage with professionals on topics like DevOps, including DevOps automation and Direct DevOps. While LinkedIn sub-communities can offer diverse perspectives, they may sometimes become cluttered with less relevant content.

5. Amazon Web Services Community Builders#

The AWS Community Builders program supports AWS enthusiasts and thought leaders by providing resources, education, and networking opportunities. This program aims to build connections with AWS product teams and other community members, though it has limited annual slots.

6. DevOps.com#

DevOps.com is a dedicated platform for DevOps news, product reviews, opinion articles, strategies, and best practices. It offers extensive resources including case studies and downloadable ebooks.

7. Atlassian DevOps Blog#

Atlassian’s blog covers a range of topics related to DevOps technologies, such as DevOps as a service, automation, and culture. It offers insights on various Atlassian products and provides guidance on adopting DevOps practices.

8. DevOps Cube#

DevOps Cube offers a wealth of resources on DevOps tools, trends, and best practices. It features articles suitable for both beginners and experienced DevOps engineers, covering tools like Docker and Jenkins.

9. Women Who Code#

Women Who Code is a global non-profit supporting women in technology through events, coding tools, mentorship, and more. The community focuses on platforms like Cloud DevOps, Azure DevOps, and AWS DevOps.

10. Hashnode#

Hashnode is an online platform where developers can engage in discussions, share knowledge, and ask questions. It facilitates interactions among professionals globally and allows for content creation and community-building activities.

Conclusion on DevOps as a Service Communities#

Developer communities have evolved from technical groups to more supportive networks where sharing information and helping others are common practices. Here's to a new era of community-driven knowledge and collaboration.

A hybrid cloud environment combines on-premises infrastructure, private cloud services, and a public cloud, with orchestration across multiple platforms. If you use a mixture of public clouds, on-premises computing, and private clouds in your data center, you have a hybrid cloud infrastructure.

We recognize the significance of hybrid cloud in cloud computing and its role in organizational development. In this blog article, we'll explore the top five characteristics that define powerful and practical hybrid cloud computing.

What is Hybrid Cloud Computing?#

A hybrid cloud computing approach combines a private cloud (or on-premises data center) with one or more public cloud products connected by public or private networks [(Tariq, 2018)]. Consistent operations enable the public cloud to serve as an extension of a private or on-premises system, with equivalent management processes and tools. Because nearly no one nowadays relies solely on the public cloud, hybrid cloud computing options are becoming increasingly popular. Companies have invested millions of dollars and thousands of hours in on-premises infrastructure. Combining a public and private cloud environment, such as an on-premises data center and a public cloud computing environment, is a common example of hybrid cloud computing provided by AWS, Microsoft Azure, and Google Cloud.

Hybrid Cloud Providers#

The digital revolution has radically changed the IT sector with the introduction of cloud computing. There are several hybrid cloud providers on the market, including:

1. Amazon Web Services (AWS)
2. Microsoft Azure
3. Google Cloud
4. VMware
5. VMware Cloud on AWS, VMware Cloud on Dell EMC, HCI powered by VMware vSAN, and VMware vRealize cloud management
6. Rackspace
7. Red Hat OpenShift
8. Hewlett Packard Enterprise
9. Cisco HyperFlex solutions
10. Nife Cloud Computing

Characteristics of Hybrid Cloud Computing#

Characteristic #1: Speed#

The capacity to automatically adjust to changes in demand is critical for innovation and competitiveness. The market expects updates immediately, and rivals are optimizing rapidly. Hybrid computing must be quick and portable, with maximum flexibility. Technologies like Docker and hybrid cloud providers such as IBM Bluemix facilitate this agility in a virtualized environment.

Characteristic #2: Cost Reduction#

One advantage of cloud computing is lowering expenses. Previously, purchasing IT assets meant paying for unused capacity, impacting the bottom line. Hybrid computing reduces IT costs while allowing enterprises to pay only for what they use. This optimization frees up funds for innovation and market introduction, potentially saving enterprises up to 30%.

Characteristic #3: Intelligent Capabilities and Automation#

Creating a digital experience in hybrid cloud computing requires integrating various technologies, which can be challenging for DevOps teams traditionally relying on numerous tools [(Aktas, 2018)]. Leveraging intelligent, unified, and centralized management capabilities enhances productivity and flexibility. IT automation in hybrid computing reduces human error, enforces policies, supports predictive maintenance, and fosters self-service habits.

Characteristic #4: Security#

Hybrid computing provides critical control over data and enhanced security by reducing data exposure. Organizations can decide where to store data based on compliance, regulatory, or security concerns. Hybrid architectures also support centralized security features like encryption, automation, access control, orchestration, and endpoint security, which are crucial for disaster recovery and data insurance [(Gordon, 2016)].

Characteristic #5: Lightweight Applications#

The final characteristic pertains to application size. DevOps teams need to develop agile apps that load quickly, boost efficiency, and occupy minimal space. Despite inexpensive storage, the focus should be on managing and understanding client data. Hybrid cloud computing supports DevOps in creating applications for global markets while meeting technological demands.

References#

Aktas, M.S. (2018). Hybrid cloud computing monitoring software architecture. Concurrency and Computation: Practice and Experience, 30(21), p.e4694. doi:10.1002/cpe.4694.

Diaby, T. and Rad, B.B. (2017). Cloud computing: a review of the concepts and deployment models. International Journal of Information Technology and Computer Science, 9(6), pp.50-58.

Gordon, A. (2016). The Hybrid Cloud Security Professional. IEEE Cloud Computing, 3(1), pp.82–86. doi:10.1109/mcc.2016.21.

Lee, I. (2019). An optimization approach to capacity evaluation and investment decision of hybrid cloud: a corporate customer's perspective. Journal of Cloud Computing, 8(1). doi:10.1186/s13677-019-0140-0.

Tariq, M.I. (2018). Analysis of the effectiveness of cloud control matrix for hybrid cloud computing. International Journal of Future Generation Communication and Networking, 11(4), pp.1-10.

Read more on Hybrid Cloud Computing: All You Need to Know About Hybrid Cloud Deployment

5G and edge computing are creating plenty of new income opportunities in industries like manufacturing, transportation, and gaming. How can communication service providers acquire a competitive advantage? Everything you need to know is provided here.

What is Telco Edge?#

Telecommunications companies frequently associate edge computing with mobile edge computing or multi-access edge computing - computing at the network's edge. Telco edge computing, on the other hand, comprises workloads operating on client-premises equipment and other points of presence at the customer site. The term "telco edge" refers to distributed computation maintained by the operator that may extend beyond the network edge and onto the customer edge. Telco Edge combines the advantages of both local and cloud computing. Telco edge computing should be adaptable and scalable. Telco edge computing can handle unexpected surges in workloads caused by increased end-user activity or answer organizations' need to grow fast while building, testing, and deploying new applications [(Klas, 2017)].

What exactly is Telco Edge Cloud (TEC)?#

The Telco Edge Cloud is a worldwide platform solution for exposing, managing, and marketing Edge Computing, Network resources, and capabilities across multiple operators and national borders, utilising existing and future network assets. Telco Edge Cloud is building a platform built on open technologies and telecom standards. MNOs may monetize their edge resources thanks to Telco Edge Cloud.

The Telco Edge Cloud idea and architecture are beneficial not only to MNOs but may also be utilised by other service and edge providers to improve their services since capabilities like NaaS are made available to these third parties [(Baliosian et al., 2021)]. Other edge and cloud providers can give methods to their application development communities to optimise edge application performance and experience by consuming Telco Edge Cloud NaaS capabilities and implementing them into their platform offerings.

Telco Edge Computing#

Telco Edge computing is also known as Mobile Edge Computing (MEC) or Multi-Access Edge Computing (MAEC). Telco Edge computing provides execution resources for applications that need networking close to end users, often within or near the operator network's boundary [(Gebhardt et al., 2012)].

Telco Edge computing may also be installed on corporate premises. Communication service providers or service providers can manage or host the edge infrastructure. Several use cases necessitate the deployment of distinct apps at multiple locations. In such cases, a distributed cloud may be viewed as an execution environment for applications spread over numerous sites, with connections maintained as a single solution. The key advantages of Telco Edge computing are low latency, high bandwidth, device processing, and data offload, and trusted computing and storage.

What is a 5G Telco Cloud?#

A 5G Telco Cloud is a software-based cloud architecture that allows for the placement of 5G network functions/applications and the division of a single infrastructure into various network slices for the delivery of a wide variety of services ranging from eMBB to URLLC [(Gebremariam et al., 2021)]. It enables you to swiftly add services, respond fast, and manage resources efficiently and automatically.

Network function virtualization, software-defined networks (SDN), edge computing, and microservices are components of 5G Telco Cloud.

1. Network Functions Virtualization of 5G Telco Cloud enables you to abstract operations from hardware. This enables conventional servers to execute operations that would otherwise necessitate the utilization of hardware.

2. Software-Defined networking (SDN), a new backhaul/mid-haul design of 5G Telco Cloud, is adaptive, manageable, and versatile. It is perfect for the fluidity of 5G applications. This design isolates network control from forwarding services, allowing network control to be programmed directly.

3. Microservices are a method of separating applications and network operations into loosely linked systems. DevOps cycles or CI/CD can be used to manage them.

5g and Edge computing#

5G and edge computing are intricately related technologies: both are set to greatly increase application performance and enable massive volumes of data to be handled in real-time. 5G speeds can be up to 10 times faster than 4G, while mobile edge computing minimises latency by putting computational capabilities closer to the end user.

5G and edge computing are technologies that can work together to power a new generation of smart devices and apps. 5G's enhanced performance can improve edge computing applications by lowering latency, improving application response times, and enhancing organizations' capacity to gather and analyze data.

Benefits of the Relationship between 5G and Edge Computing#

Ultra-low latency use cases: The combination of 5G with edge computing is important for achieving ultra-low latency in a variety of edge devices and use cases.

Near real-time performance: Using 5G and edge computing together allows organizations to collect and process large amounts of real-time data to optimize various operational processes and increase productivity and customer experiences.

Improved bandwidth usage: The connection between 5G and edge computing influences the success of 5G network technologies.

The lack of a standardized methodology for evaluating Cloud Service Providers (CSPs), along with the reality that no two Cloud Service Providers are alike, complicates the process of picking the best one for your firm. This post will help you work through the characteristics you may use to pick a supplier that can best meet your organization's technological and operational demands.

So, how do you go about selecting a Cloud hosting provider? To begin, it is useful to understand who the primary players are today.

The Players#

The sector is crowded, with the big three — AWS, Microsoft Azure, and Google Cloud Services — as well as smaller specialized firms. Of course, AWS, Google Cloud Services, and Azure reign supreme. There are many cloud providers in Singapore such as NIFE, which is a developer-friendly serverless platform designed to let businesses quickly manage, deploy, and scale applications globally.

Criteria for Primary Evaluation#

When deciding which Cloud Service Providers to utilize, consider the alternatives that different providers supply and how they will complement your specific company characteristics and objectives. The following are the main factors to consider for practically any business:

1. Cloud Security#

You want to know exactly what your security objectives are, the security measures provided by each provider, and the procedures they employ to protect your apps and data. Furthermore, ensure that you properly grasp the exact areas for which each party is accountable.

Security is a primary priority in Cloud Computing Services, therefore it's vital to ask specific questions about your specific use cases, industry, legal needs, and any other issues you may have [(Kumar and Goyal, 2019)]. Do not fail to assess this key element of functioning in the cloud.

2. Cloud Compliance#

Next, select a Cloud Computing Service that can assist you in meeting compliance criteria specific to your sector and business. Whether you are subject to GDPR, SOC 2, PCI DSS, HIPAA, or another standard, ensure that you understand what it will take to accomplish compliance once your apps and data are housed on a public cloud architecture [(Brandis et al., 2019)]. Make sure you understand your duties and which parts of compliance the supplier will assist you in checking off.

3. Architecture#

Consider how the architecture will be integrated into your processes today and in the future when selecting a cloud provider. If your company depends heavily on Amazon or Google Cloud Services, it could be wise to go to such Cloud hosting providers for ease of integration and consolidation. When making your selection, you should also consider cloud storage designs. When it comes to storage, the three major suppliers have comparable architectures and offer a variety of storage options to meet a variety of demands, but they all have various forms of archive storage [(Narasayya and Chaudhuri, 2021)].

4. Manageability#

You should also spend some time establishing what different [Cloud hosting providers] will need you to handle. Each service supports several orchestration tools and interfaces with a variety of other services. If your firm relies heavily on certain services, ensure that the cloud provider you select has a simple method to interface with them.

Before making a final selection, you should assess how much time and effort it will take your team to handle various components of the cloud infrastructure.

5. Service Levels#

This aspect is critical when a company's availability, reaction time, capacity, and support requirements are stringent. Cloud Service Level Agreements (Cloud SLAs) are an essential consideration when selecting a provider. Legal considerations for the security of data hosted in the cloud service, particularly in light of GDPR rules, should also be given special consideration [(World Bank, 2022)]. You must be able to rely on your cloud service provider to do the correct thing, and you must have a legal agreement in place to protect you when something goes wrong.

6. Support#

Another factor that must be carefully considered is support. In certain circumstances, the only way to receive help is through a chat service or a contact center. You may or may not find this acceptable. In other circumstances, you may have access to a specialized resource, but there is a significant likelihood that time and access will be limited. Before selecting a Cloud Computing Services, inquire about the amount and type of assistance you will receive. The cloud providers in Singapore like NIFE provide excellent customer support.

7. Costs#

While cost should never be the sole or most essential consideration, there is no disputing that price will play a significant influence in determining which cloud service providers you use.

8. Container Capabilities#

If your company wants to move its virtual server workloads to containers, container orchestration, managed containers, and/or serverless architecture, you should thoroughly examine each Cloud hosting provider's container capabilities. The cloud providers in Singapore like NIFE use Docker Containers.

References#

Brandis, K., Dzombeta, S., Colomo-Palacios, R. and Stantchev, V. ([2019]). Governance, Risk, and Compliance in Cloud Scenarios. Applied Sciences, online 9(2), p.320. doi:10.3390/app9020320.

Kumar, R. and Goyal, R. ([2019]). On cloud security requirements, threats, vulnerabilities and countermeasures: A survey. Computer Science Review, 33, pp.1-48. doi:10.1016/j.cosrev.2019.05.002.

Narasayya, V. and Chaudhuri, S. ([2021]). Cloud Data Services: Workloads, Architectures and Multi-Tenancy. Foundations and Trends® in Databases, 10(1), pp.1-107. doi:10.1561/1900000060.

World Bank. ([2022]). Government Migration to Cloud Ecosystems: Multiple Options, Significant Benefits, Manageable Risks.

Wu, Y., Lei, L., Wang, Y., Sun, K. and Meng, J. ([2020]). Evaluation on the Security of Commercial Cloud Container Services. Lecture Notes in Computer Science, pp.160-177. doi:10.1007/978-3-030-62974-8_10.

DevOps is the answer if you want to produce better software quicker. This software development process invites everyone to the table to swiftly generate secure code. Through automation, collaboration, rapid feedback, and iterative improvement, DevOps principles enable software developers (Devs) and operations (Ops) teams to speed delivery.

What exactly is DevOps as a Service?#

Many mobile app development organisations across the world have adopted the DevOps as a Service mindset. It is a culture that every software development company should follow since it speeds up and eliminates risk in software development [(Agrawal and Rawat, 2019)].

The primary rationale for providing DevOps as a service to clients is to transition their existing applications to the cloud and make them more stable, efficient, and high-performing. The primary goal of DevOps as a service is to ensure that the modifications or activities performed during software delivery are trackable. Applying DevOps practices such as Continuous Integration and Continuous Delivery enables businesses to generate breakthrough results and outstanding commercial value from software [(Trihinas et al., 2018)].

As more organisations adopt DevOps and transfer their integrations to the cloud, the tools used in build, test, and deployment processes will also travel to the cloud, thereby turning continuous delivery into a managed cloud service.

DevOps as a Managed Cloud Service#

What exactly is DevOps in the cloud? It is essentially the migration of your continuous delivery tools and procedures to a hosted virtual platform. The delivery pipeline is reduced to a single site in which developers, testers, and operations specialists work together as a team, and as much of the deployment procedure as feasible is automated. Here are some of the most prominent commercial choices for cloud-based DevOps.

AWS Direct DevOps Tools and Services#

Amazon Web Services (AWS) has established a strong worldwide network to virtualize some of the world's most complicated IT settings [(Alalawi, Mohsin and Jassim, 2021)]. AWS Direct DevOps is a quick and relatively straightforward option to transfer your DevOps to the cloud, with fibre-connected data centres located all over the world and a payment schedule that measures exactly the services you use down to the millisecond of computing time. Even though AWS Direct DevOps provides a plethora of sophisticated interactive capabilities, three specific services are at the heart of continuous cloud delivery.

AWS CodeBuild: AWS CodeBuild is a completely managed service for generating code, automating quality assurance testing, and delivering deployment-ready software.

AWS CodePipeline: You define parameters and develop the model for your ideal deployment scenario using a beautiful graphic interface, and CodePipeline handles it from there.

AWS CodeDeploy: When a fresh build passes through CodePipeline, CodeDeploy distributes the functioning package to each instance based on the settings you specify.

Google Cloud DevOps Tools and Services#

The search engine giant boasts an unrivalled global network, user-friendly interfaces, and an ever-expanding set of features that make the Google Cloud DevOps option worthwhile to explore.

Google Cloud DevOps also offers comprehensive cloud development suites for a broad range of platforms, including Visual Studio, Android Studio, Eclipse, Powershell, and many more [(Jindal and Gerndt, 2021)]. In a cloud environment, use the development tools you already know and love.

Let's take a look at some of the most powerful StackDriver development tools available from Google.

Stackdriver Monitoring: Get a visual representation of your environment's health and pain areas.

Stackdriver Debugger: Zoom in on any code position to see how your programme reacts in real-time production.

Stackdriver Logging: Ingest, monitor, and respond to crucial log events.

StackDriver Trace: Locate, examine, and show latencies in the Google Cloud Console.

Microsoft Azure DevOps Tools and Services#

Microsoft Azure DevOps, Microsoft's cloud management platform, is bringing a powerful punch to DevOps as a managed service area. Azure, like AWS Direct DevOps and Google Cloud DevOps, provides a remarkable range of creative and compatible DevOps tools.

With so many enterprises already invested in Microsoft goods and services, Microsoft Azure DevOps may provide the simplest path to hybrid or full cloud environments. Microsoft's critical DevOps tools include the following:

Azure Application Service: Microsoft Azure DevOps App Service offers an infinite number of development alternatives.

Azure DevTest Labs: Azure DevTest Labs simplifies experimentation for your DevOps team.

Azure Stack: Azure Stack is a solution that allows you to integrate Azure services into your current data centre [(Soh et al., 2020)].

The Advantages of DevOps as a Service#

[DevOps as a Service] has several advantages. Some of the more notable advantages are listed below:

• Better collaboration
• More rapid testing and deployment
• Reduces complexity
• Product of the highest quality
• Internal DevOps coexists

Final thoughts#

Choosing DevOps as a service will allow you to develop your business faster and provide more value to your clients. Choosing DevOps as a service is your route to customer success, whether you're developing a new application or upgrading your legacy ones.

As a developer, you're likely familiar with new technologies that promise to enhance software production speed and app robustness once deployed. Cloud computing technology is a prime example, offering immense promise. This article delves into multi-access edge computing and deployment in cloud computing, providing practical advice to help you with real-world application deployments on cloud infrastructure.

Why is Cloud Simplification Critical?#

Complex cloud infrastructure often results in higher costs. Working closely with cloud computing consulting firms to simplify your architecture can help reduce these expenses [(Asmus, Fattah, and Pavlovski, 2016)]. The complexity of cloud deployment increases with the number of platforms and service providers available.

The Role of Multi-access Edge Computing in Application Deployment#

[Multi-access Edge Computing] offers cloud computing capabilities and IT services at the network's edge, benefiting application developers and content providers with ultra-low latency, high bandwidth, and real-time access to radio network information. This creates a new ecosystem, allowing operators to expose their Radio Access Network (RAN) edge to third parties, thus offering new apps and services to mobile users, corporations, and various sectors in a flexible manner [(Cruz, Achir, and Viana, 2022)].

Choose Between IaaS, PaaS, or SaaS#

In cloud computing, the common deployment options are Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). PaaS is often the best choice for developers as it manages infrastructure, allowing you to focus on application code.

Scale Your Application#

PaaS typically supports scalability for most languages and runtimes. Developers should understand different scaling methods: vertical, horizontal, manual, and automatic [(Eivy and Weinman, 2017)]. Opt for a platform that supports both manual and automated horizontal scaling.

Consider the Application's State#

Cloud providers offering PaaS often prefer greenfield development, which involves new projects without constraints from previous work. Porting existing or legacy deployments can be challenging due to ephemeral file systems. For greenfield applications, create stateless apps. For legacy applications, choose a PaaS provider that supports both stateful and stateless applications.

Select a Database for Cloud-Based Apps#

If your application doesn't need to connect to an existing corporate database, your options are extensive. Place your database in the same geographic location as your application code but on separate containers or servers to facilitate independent scaling of the database [(Noghabi, Kolb, Bodik, and Cuervo, 2018)].

Consider Various Geographies#

Choose a cloud provider that enables you to build and scale your application infrastructure across multiple global locations, ensuring a responsive experience for your users.

Use REST-Based Web Services#

Deploying your application code in the cloud offers the flexibility to scale web and database tiers independently. This separation allows for exploring technologies you may not have considered before.

Implement Continuous Delivery and Integration#

Select a cloud provider that offers integrated continuous integration and continuous delivery (CI/CD) capabilities. The provider should support building systems or interacting with existing non-cloud systems [(Garg and Garg, 2019)].

Prevent Vendor Lock-In#

Avoid cloud providers that offer proprietary APIs that can lead to vendor lock-in, as they might limit your flexibility and increase dependency on a single provider.

References

Asmus, S., Fattah, A., & Pavlovski, C. ([2016]). Enterprise Cloud Deployment: Integration Patterns and Assessment Model. IEEE Cloud Computing, 3(1), pp.32-41. doi:10.1109/mcc.2016.11.

Cruz, P., Achir, N., & Viana, A.C. ([2022]). On the Edge of the Deployment: A Survey on Multi-Access Edge Computing. _ACM Computing Surveys (CSUR).

Eivy, A., & Weinman, J. ([2017]). Be Wary of the Economics of ‘Serverless' Cloud Computing. IEEE Cloud Computing, 4(2), pp.6-12. doi:10.1109/mcc.2017.32.

Garg, S., & Garg, S. ([2019]). Automated Cloud Infrastructure, Continuous Integration, and Continuous Delivery Using Docker with Robust Container Security. In 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR) (pp. 467-470). IEEE.

Noghabi, S.A., Kolb, J., Bodik, P., & Cuervo, E. ([2018]). Steel: Simplified Development and Deployment of Edge-Cloud Applications. In 10th USENIX Workshop on Hot Topics in Cloud Computing (HotCloud 18).

There are several definitions of DevOps, and many of them sufficiently explain one or more characteristics that are critical to finding flow in the delivery of IT services. Instead of attempting to provide a complete description, we want to emphasize DevOps principles that we believe are vital when adopting or shifting to a DevOps method of working.

What is DevOps?#

DevOps is a software development culture that integrates development, operations, and quality assurance into a continuous set of tasks (Leite et al., 2020). It is a logical extension of the Agile technique, facilitating cross-functional communication, end-to-end responsibility, and cooperation. Technical innovation is not required for the transition to DevOps as a service.

Principles of DevOps#

DevOps is a concept or mentality that includes teamwork, communication, sharing, transparency, and a holistic approach to software development. DevOps is based on a diverse range of methods and methodologies. They ensure that high-quality software is delivered on schedule. DevOps principles govern the service providers such as AWS Direct DevOps, Google Cloud DevOps, and Microsoft Azure DevOps ecosystems.

Principle 1 - Customer-Centric Action#

Short feedback loops with real consumers and end users are essential nowadays, and all activity in developing IT goods and services revolves around these clients.

To fulfill these consumers' needs, DevOps as a service must have : - the courage to operate as lean startups that continuously innovate, - pivot when an individual strategy is not working - consistently invest in products and services that will provide the highest degree of customer happiness.

AWS Direct DevOps, Google Cloud DevOps, and Microsoft Azure DevOps are customer-oriented DevOps.

Principle 2 - Create with the End in Mind.#

Organizations must abandon waterfall and process-oriented models in which each unit or employee is responsible exclusively for a certain role/function and is not responsible for the overall picture. They must operate as product firms, with an explicit focus on developing functional goods that are sold to real consumers, and all workers must share the engineering mentality necessary to imagine and realise those things (Erich, Amrit and Daneva, 2017).

Principle 3 - End-to-end Responsibility#

Whereas conventional firms build IT solutions and then pass them on to Operations to install and maintain, teams in a DevOps as a service are vertically structured and entirely accountable from idea to the grave. These stable organizations retain accountability for the IT products or services generated and provided by these teams. These teams also give performance support until the items reach end-of-life, which increases the sense of responsibility and the quality of the products designed.

Principle 4 - Autonomous Cross-Functional Teams#

Vertical, fully accountable teams in product organizations must be completely autonomous throughout the whole lifecycle. This necessitates a diverse range of abilities and emphasizes the need for team members with T-shaped all-around profiles rather than old-school IT experts who are exclusively informed or proficient in, say, testing, requirements analysis, or coding. These teams become a breeding ground for personal development and progress (Jabbari et al., 2018).

Principle 5 - Continuous Improvement#

End-to-end accountability also implies that enterprises must constantly adapt to changing conditions. A major emphasis is placed on continuous improvement in DevOps as a service to eliminate waste, optimize for speed, affordability, and simplicity of delivery, and continually enhance the products/services delivered. Experimentation is thus a vital activity to incorporate and build a method of learning from failures. In this regard, a good motto to live by is "If it hurts, do it more often."

Principle 6 - Automate everything you can#

Many firms must minimize waste to implement a continuous improvement culture with high cycle rates and to develop an IT department that receives fast input from end users or consumers. Consider automating not only the process of software development, but also the entire infrastructure landscape by constructing next-generation container-based cloud platforms like AWS Direct DevOps, Google Cloud DevOps, and Microsoft Azure DevOps that enable infrastructure to be versioned and treated as code (Senapathi, Buchan and Osman, 2018). Automation is connected with the desire to reinvent how the team provides its services.

Remember that a DevOps Culture Change necessitates a Unified Team.#

DevOps is just another buzzword unless key concepts at the foundation of DevOps are properly implemented. DevOps concentrates on certain technologies that assist teams in completing tasks. DevOps, on the other hand, is first and foremost a culture. Building a DevOps culture necessitates collaboration throughout a company, from development and operations to stakeholders and management. That is what distinguishes DevOps from other development strategies.

Remember that these concepts are not fixed in stone while shifting to DevOps as a service. DevOps Principles should be used by AWS Direct DevOps, Google Cloud DevOps, and Microsoft Azure DevOps according to their goals, processes, resources, and team skill sets.

Organizations continue to build new apps on the cloud or move current applications to the cloud. A company that adopts cloud technologies and/or selects cloud service providers (CSPs) and services or applications without first thoroughly understanding the hazards associated exposes itself to a slew of commercial, economic, technological, regulatory, and compliance hazards. In this blog, we will learn about the hazards of application deployment, Cloud Deployment, Deployment in Cloud Computing, and Cloud deployment models in cloud computing.

What is Cloud Deployment?#

Cloud computing is a network access model that enables ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or interaction from service providers [(Moravcik, Segec and Kontsek, 2018)].

Essential Characteristics:#

1. On-demand self-service
2. Broad network access
3. Resource pooling
4. Rapid elasticity
5. Measured service

Service Models:#

1. Software as a service (SaaS)
2. Platform as a service (PaaS)
3. Infrastructure as a service (IaaS)

Deployment Models:#

1. Private Cloud
2. Community cloud
3. Public cloud
4. Hybrid cloud

Hazards of Application Deployment on Clouds#

At a high level, cloud environments face the same hazards as traditional data centre settings; the threat landscape is the same. That is, deployment in cloud computing runs software, and software contains weaknesses that attackers aim to exploit.

1. Consumers now have less visibility and control.

When businesses move assets/operations to the cloud, they lose visibility and control over those assets/operations. When leveraging external cloud services, the CSP assumes responsibility for some rules and infrastructure in Cloud Deployment.

2. On-Demand Self-Service Makes Unauthorized Use Easier.

CSPs make it very simple to add Cloud deployment models in cloud computing. The cloud's on-demand self-service provisioning features enable an organization's people to deploy extra services from the agency's CSP without requiring IT approval. Shadow IT is the practice of employing software in an organisation that is not supported by the organization's IT department.

3. Management APIs that are accessible through the internet may be compromised.

Customers employ application programming interfaces (APIs) exposed by CSPs to control and interact with cloud services (also known as the management plane). These APIs are used by businesses to provide, manage, choreograph, and monitor their assets and people. CSP APIs, unlike management APIs for on-premises computing, are available through the Internet, making them more vulnerable to manipulation.

4. The separation of several tenants fails.

Exploiting system and software vulnerabilities in a CSP's infrastructure, platforms, or applications that allow multi-tenancy might fail to keep tenants separate. An attacker can use this failure to obtain access from one organization's resource to another user's or organization's assets or data.

5. Incomplete data deletion

Data deletion threats emerge because consumers have little insight into where their data is physically housed in the cloud and a limited capacity to verify the secure erasure of their data. This risk is significant since the data is dispersed across several storage devices inside the CSP's infrastructure in a multi-tenancy scenario.

6. Credentials have been stolen.

If an attacker acquires access to a user's cloud credentials, the attacker can utilise the CSP's services such as deployment in cloud computing to provide new resources (if the credentials allow provisioning) and target the organization's assets. An attacker who obtains a CSP administrator's cloud credentials may be able to use them to gain access to the agency's systems and data.

7. Moving to another CSP is complicated by vendor lock-in.

When a company contemplates shifting its deployment in cloud computing from one CSP to another, vendor lock-in becomes a concern. Because of variables such as non-standard data formats, non-standard APIs, and dependency on one CSP's proprietary tools and unique APIs, the company realises that the cost/effort/schedule time required for the transition is substantially more than previously estimated.

8. Increased complexity puts a strain on IT staff.

The transition to the cloud can complicate IT operations. To manage, integrate, and operate in Cloud deployment models in cloud computing, the agency's existing IT employees may need to learn a new paradigm. In addition to their present duties for on-premises IT, IT employees must have the ability and skill level to manage, integrate, and sustain the transfer of assets and data to the cloud.

Conclusion

It is critical to note that CSPs employ a shared responsibility security approach. Some features of security are accepted by the CSP. Other security concerns are shared by the CSP and the consumer. Finally, certain aspects of security remain solely the consumer's responsibility. Effective Cloud deployment models in cloud computing and cloud security are dependent on understanding and fulfilling all customs duties. The inability of consumers to understand or satisfy their duties is a major source of security issues in Cloud Deployment.

What is the hybrid cloud architecture?#

Individually managing public and private cloud resources is preferable to uniformly managing cloud environments because it reduces the likelihood of process redundancy. By limiting the exposure of private data to the public cloud, a hybrid cloud architecture can eliminate many security risks. A hybrid cloud deployment infrastructure typically consists of a public infrastructure as a service (IaaS) platform, a private cloud or data centre, and network access. Many hybrid cloud deployment models make use of both local area networks (LAN) and wide area networks (WAN).

What is the purpose of a hybrid cloud?#

[Hybrid clouds] can also be used to create multi-cloud environments, giving businesses more options for where they want their data stored and how they want it accessed. By allowing businesses to back up data in both public and private clouds, a hybrid cloud deployment environment can be beneficial for disaster recovery.

What are the benefits of hybrid cloud deployment?#

Governance of applications that works: A hybrid cloud method allows you to choose where your application will run and where hybrid computing will take place [(Kaviani, Wohlstadter and Lea, 2014)]. This can assist to increase privacy while also ensuring compliance for your regulated apps.

Enhanced speed and decreased latency: A hybrid cloud solution might sometimes assist dispersed programmes in faraway regions. Hybrid computing occurs near the end consumers for applications with low latency needs.

Flexible operations: Hybrid computing allows you to function in an environment that is ideal for you. You may, for example, construct portable apps and simply migrate between public and private clouds by creating using containers.

Better ROI: You may increase your cloud computing capacity without raising your data centre costs by adding a public cloud provider to your existing on-premises architecture.

Hybrid Cloud Deployment Models#

Hybrid cloud deployment models are classified into three types:

Hybrid cloud deployment model architecture with a phased migration

You migrate applications or workloads from an on-premises data centre to the architecture of a public cloud service provider. This can be done gradually or all at once. This paradigm has the advantage of allowing you to use only what you need, assigning as much or as little as needed for each application or transaction. The negative is that it may not provide you as much control over how things work as if they were on using a private cloud deployment model [(Biswas and Verma, 2020)].

Hybrid cloud deployment model with apps that are only partially integrated

This concept entails migrating some but not all apps or transactions to the public cloud while maintaining others on-premises. If your organisation has apps that can operate in private cloud deployment model settings or public clouds like AWS or Azure, this is a terrific solution. Based on performance requirements or financial limits, you may determine which ones are a better fit for each case.

Hybrid cloud deployment model with integrated apps

The hybrid cloud strategy with integrated apps entails integrating applications running a private cloud deployment model and in the public cloud utilising PaaS software on the public cloud. The applications on the private cloud deployment model are installed using IaaS software and then integrated into the public cloud using PaaS software.

Is Hybrid Cloud the Best Option for Me?#

Hybrid cloud deployments are a popular choice for businesses that want to take advantage of cloud computing's flexibility and cost benefits while keeping control over their data and applications. To accomplish the intended business objective, hybrid cloud deployment often employs private, public, and third-party resources.

Hybrid Cloud Deployment Environment#

The following approaches can be used to deploy hybrid clouds:

Non-critical workloads should be outsourced to a public cloud: You can outsource a mission-critical system that does not require quick response times, such as a human resources application, to a public cloud provider [(Sturrus and Kulikova, 2014)]. This allows you to host and maintain applications on the public cloud while maintaining control over your data.

Use a virtual private cloud to deploy mission-critical workloads: The alternative is to host important workloads in a virtual private cloud (VPC). It is also the most widely used hybrid cloud deployment option since it mixes on-premises infrastructure with public cloud resources.

Dedicated hardware should be used to host the private cloud: Instead of depending entirely on public or private clouds, you host your private infrastructure on the private cloud deployment model's hardware under this architecture.

Multi-access edge computing. Server computing power has traditionally been utilised to execute activities such as data reduction or the creation of complex distributed systems. Such 'intelligent' operations are handled by servers in the cloud model so that they may be moved to other devices with little or no computational capacity.

Why Edge Cloud?#

Edge cloud shifts a large portion of these processing chores to the client side, which is known as Edge Computing for Enterprises. Edge Network computing often refers to IoT devices, but it may also apply to gaming hardware that processes telemetry on the device rather than transmitting it to the cloud. This opens up several potentials for enterprises, particularly when it comes to providing low-latency services across apps or high-density platform utilisation using Multi-access edge computing.

Why is an edge to cloud connectivity required?#

The increased requirement for real-time data-driven decision-making, particularly by Edge Computing for Enterprises, is one driver of today's edge-to-cloud strategy [(Pastor-Vargas et al., 2020)]. For example, autonomous vehicle technologies rely on artificial intelligence (AI) and machine learning (ML) systems that can discern whether an item on the roadway is another car, a human, or road debris in a fraction of a second.

What is an edge-to-cloud platform?#

An edge-to-cloud platform is intended to provide a Cloud Computing technology and experience to all of an organization's apps and data, independent of location. It provides a uniform user experience and prioritizes security in its design. It also enables enterprises to seek new business prospects by providing new services with a point-and-click interface and easy scalability to suit changing business demands.

How is an edge-to-cloud platform work?#

To provide a cloud experience everywhere, a platform must have certain distinguishing features:

Self-service: Organizations want the ability to swiftly and simply spin up resources for new initiatives, such as Edge Computing for Enterprises, new virtual machines (VMs), or container or MLOps services. Users may pick and deploy the cloud services they require with a single click.

Rapid scalability: To deliver on the cloud's promise of agility, a platform must incorporate built-in buffer capacity, so that when additional capacity is required, it is already installed and ready to go [(Osia et al., 2018)].

Pay-as-you-go: Payment should be based on the real capacity used, allowing firms to launch new initiatives without incurring large upfront expenses or incurring procurement delays.

Managed on your behalf: An edge-to-cloud platform should alleviate the operational load of monitoring, updating infrastructure and utilising Multi-access edge computing, allowing IT to concentrate on growing the business and producing revenue.

Why is an edge-to-cloud approach required?#

Organizations throughout the world are embracing digital transformation by using Edge Computing for Enterprises, but in many cases, their existing technological infrastructure must be re-examined to meet the needs of data growth, Edge networks, IoT, and remote workforces [(Nezami et al., 2021)]. A single experience with the same agility, simplicity, and pay-per-use flexibility across an organization's whole hybrid IT estate is provided via an edge-to-cloud strategy and Multi-access edge computing. This implies that enterprises no longer have to make concessions to operate mission-critical programmes, and essential enterprise data services may now access both on-premises and public Cloud Computing technology resources.

What does this signify for your network design?#

By merging Edge Computing for Enterprises and Cloud Computing technology, you may make use of the power of distributed systems by processing data on devices that then transfer it to the cloud. It can be processed, analysed, or saved here with minimal (or even no) processing power. Because of an Edge Network and cloud architecture, linked automobiles that exchange information, for example, may analyse data without relying on a server's processing capability.

What are the Advantages of Edge -to- Cloud Computing technology?#

Organizations benefit from the edge-to-cloud experience in several ways:

• Increase agility: Edge Networks and cloud solutions enable enterprises to respond rapidly to business needs, capitalise on market opportunities as they occur, and reduce time to market for new products.

• Application modernization: Even mission-critical workloads that are not suitable for moving to the public cloud may be performed efficiently on today's as-a-service platforms.

• Make use of the capabilities of hybrid cloud systems without complications: The edge-to-cloud platform provides the benefits of hybrid cloud adoption and Multi-access edge computing without the associated administrative issues. The user experience of applications operating on an as-a-service platform remains consistent.

• With Edge-to-Cloud Computing technology, enterprises can simply establish the ideal blend of on- and off-premises assets and swiftly move between them when business and market conditions change (Milojicic, 2020).

Recognize the transformative power of applications and data:

Some data sets are either too vast or too important to migrate to the cloud.

Multi-access edge computing. Server computing power has traditionally been utilised to execute activities such as data reduction or the creation of complex distributed systems. Such 'intelligent' operations are handled by servers in the cloud model so that they may be moved to other devices with little or no computational capacity.

Why Edge Cloud?#

Edge cloud shifts a large portion of these processing chores to the client side, which is known as Edge Computing for Enterprises. Edge Network computing often refers to IoT devices, but it may also apply to gaming hardware that processes telemetry on the device rather than transmitting it to the cloud. This opens up several potentials for enterprises, particularly when it comes to providing low-latency services across apps or high-density platform utilisation using Multi-access edge computing.

Why is an edge to cloud connectivity required?#

The increased requirement for real-time data-driven decision-making, particularly by Edge Computing for Enterprises, is one driver of today's edge-to-cloud strategy [(Pastor-Vargas et al., 2020)]. For example, autonomous vehicle technologies rely on artificial intelligence (AI) and machine learning (ML) systems that can discern whether an item on the roadway is another car, a human, or road debris in a fraction of a second.

What is an edge-to-cloud platform?#

An edge-to-cloud platform is intended to provide a Cloud Computing technology and experience to all of an organization's apps and data, independent of location. It provides a uniform user experience and prioritizes security in its design. It also enables enterprises to seek new business prospects by providing new services with a point-and-click interface and easy scalability to suit changing business demands.

How is an edge-to-cloud platform work?#

To provide a cloud experience everywhere, a platform must have certain distinguishing features:

Self-service: Organizations want the ability to swiftly and simply spin up resources for new initiatives, such as Edge Computing for Enterprises, new virtual machines (VMs), or container or MLOps services. Users may pick and deploy the cloud services they require with a single click.

Rapid scalability: To deliver on the cloud's promise of agility, a platform must incorporate built-in buffer capacity, so that when additional capacity is required, it is already installed and ready to go [(Osia et al., 2018)].

Pay-as-you-go: Payment should be based on the real capacity used, allowing firms to launch new initiatives without incurring large upfront expenses or incurring procurement delays.

Managed on your behalf: An edge-to-cloud platform should alleviate the operational load of monitoring, updating infrastructure and utilising Multi-access edge computing, allowing IT to concentrate on growing the business and producing revenue.

Why is an edge-to-cloud approach required?#

Organizations throughout the world are embracing digital transformation by using Edge Computing for Enterprises, but in many cases, their existing technological infrastructure must be re-examined to meet the needs of data growth, Edge networks, IoT, and remote workforces [(Nezami et al., 2021)]. A single experience with the same agility, simplicity, and pay-per-use flexibility across an organization's whole hybrid IT estate is provided via an edge-to-cloud strategy and Multi-access edge computing. This implies that enterprises no longer have to make concessions to operate mission-critical programmes, and essential enterprise data services may now access both on-premises and public Cloud Computing technology resources.

What does this signify for your network design?#

By merging Edge Computing for Enterprises and Cloud Computing technology, you may make use of the power of distributed systems by processing data on devices that then transfer it to the cloud. It can be processed, analysed, or saved here with minimal (or even no) processing power. Because of an Edge Network and cloud architecture, linked automobiles that exchange information, for example, may analyse data without relying on a server's processing capability.

What are the Advantages of Edge -to- Cloud Computing technology?#

Organizations benefit from the edge-to-cloud experience in several ways:

• Increase agility: Edge Networks and cloud solutions enable enterprises to respond rapidly to business needs, capitalise on market opportunities as they occur, and reduce time to market for new products.

• Application modernization: Even mission-critical workloads that are not suitable for moving to the public cloud may be performed efficiently on today's as-a-service platforms.

• Make use of the capabilities of hybrid cloud systems without complications: The edge-to-cloud platform provides the benefits of hybrid cloud adoption and Multi-access edge computing without the associated administrative issues. The user experience of applications operating on an as-a-service platform remains consistent.

• With Edge-to-Cloud Computing technology, enterprises can simply establish the ideal blend of on- and off-premises assets and swiftly move between them when business and market conditions change (Milojicic, 2020).

Recognize the transformative power of applications and data:

Some data sets are either too vast or too important to migrate to the cloud.

Significant changes in the digital world over the last several decades have prompted businesses to seek new methods to offer information. As a result, Content Delivery Networks, or CDNs, have grown in popularity. Content Delivery Networking global servers that enable consumers to get material with minimal delay [(Goyal, Joshi and Ram, 2021)]. CDN Network is being used by an increasing number of enterprises to allow their big worldwide audience to access their services.

Benefits of Content Delivery Networking (CDN)#

1. Reduce Server Load#

Remember that a Content Delivery Networks are a globally spread network of servers used to deliver content. Because of the intentional placement of servers over huge distances, no server is at risk of being overwhelmed. This frees up total capacity, allowing for more concurrent users while lowering bandwidth and delivery costs [(Benkacem et al., 2018)].

2. Improve Website Performance and Speed#

A company may utilise CDNs to swiftly distribute high-performance website material by caching it on CDN servers nearest to end users. This content can include HTML code, picture files, dynamic content, and JavaScript. As a result, when website visitor requests a page or content, they do not have to wait for the request to be routed to the origin server.

3. Allow for Audience Segmentation Using User Analytics#

One advantage of Content Delivery Networks that is sometimes ignored is their capacity to deliver useful audience insights. User analytics such as real-time load data, capacity per customer, most active locations, and the popularity of various content assets provide a wealth of information that may be utilized to identify trends and content consumption habits. Businesses may utilize this information to assist their developers in further optimizing the website, improving the user experience, and contributing to increased sales and conversions.

4. Lower Network Latency and Packet Loss#

If these packets must travel over vast distances and through several devices before reaching the end user, some may be lost along the way. They might also be delayed, increase latency, or arrive at the end user in a different sequence than planned, causing a jitter [(Wichtlhuber, Reinecke and Hausheer, 2015)]. All of this results in a less-than-ideal end-user experience, especially when the material sent includes high-definition video, audio, or live streaming.

5. Turn on Advanced Website Security#

Improved website security is an indirect advantage of Content Delivery Networks services. This is notably useful in DDoS assaults, in which attackers attempt to overload a critical DNS server by delivering a massive amount of queries. The objective is to knock down this server and, with it, the website. Content Delivery Networking can mitigate such DDoS assaults by functioning as a DDoS protection and mitigation platform, distributing the load evenly throughout the network's whole capacity, and safeguarding data centers [(Li and Meng, 2021)].

6. Increase the Accessibility of Content#

CDN Network may absorb all of this traffic and disperse it throughout its distributed infrastructure, allowing a company to improve its content available regardless of demand. If one server fails, additional points of presence (PoPs) can pick up the traffic and keep the service running.

7. Cost Savings from Bandwidth Reduction#

CDNs are indirectly responsible for saving money and reducing unnecessary expenses and losses related to server failures and hacked websites due to their capacity to defeat one of the most popular forms of cyber assaults in the form of DDoS protection. In general, using the best CDN provider will save organizations money on the costs of putting up infrastructure, hosting, and servers all over the world.

8. Effectively Expand Audience Reach and Scale#

Content Delivery Networking makes it easier and more cost-effective to send information to consumers in locations remote from a company's headquarters and primary servers using CDN Cloud. They also help to ensure that clients have a consistent user experience. Keeping clients delighted in this manner will have a snowball effect and drive audience expansion, helping organizations to efficiently extend into new areas.

9. A CDN Allows for Global Reach#

Over one-third of the world's population is online, implying that worldwide internet use has expanded dramatically in the previous 15 years. CDN Cloud acceleration with local POPs is provided through CDNs. Because of its worldwide reach, any latency issues that disrupt long-distance online transactions and create poor load times would be eliminated.

10. Customer Service is Available 24/7#

Quality Content Delivery Networking has a reputation for providing excellent customer service among the best CDN [(Herbaut et al., 2016)]. In other words, there is always a CS team available to you. Whenever something goes wrong, you have a backup ready to assist you in resolving your performance issues. Having a support team on speed dial is a wise business move because you're not just paying for a cloud service, but for a wide range of services that will help your company flourish on a worldwide scale.

Cloud cost optimization is the process of finding underutilized resources, minimizing waste, obtaining more discounted capacity, and scaling the best cloud computing services to match the real necessary capacity—all to lower infrastructure as a service price [(Osypanka and Nawrocki, 2020)].

Nife is a Singapore-based Unified Public Cloud Edge best cloud computing platform for securely managing, deploying, and scaling any application globally using Auto Deployment from Git. It requires no DevOps, servers, or infrastructure management. There are currently many best cloud computing companies in Singapore and NIFE is one of the best cloud computing companies in Singapore.

What makes Nife the best Cloud Company in Singapore?#

Public cloud services are well-known for their pay-per-use pricing methods, which charge only for the resources that are used. However, in most circumstances, public cloud services charge cloud clients based on the resources allocated, even if those resources are never used. Monitoring and controlling cloud services is a critical component of cloud cost efficiency. This can be challenging since purchasing choices are often spread throughout a company, and people can install cloud services and commit to charges with little or no accountability [(Yahia et al., 2021)]. To plan, budget, and control expenses, a cloud cost management approach is required. Nife utilizes cloud optimization to its full extent thus making it one of the best cloud companies in Singapore.

What Factors Influence Your Cloud Costs?#

Several factors influence cloud expenses, and not all of them are visible at first.

Public cloud services typically provide four price models:

1. **Pay as you go:** Paying for resources utilized on an hourly, minutely, or secondary basis.

2. **Reserved instances:** Paying for a resource in advance, often for one or three years.

3. **Spot instances:** Buying the cloud provider's excess capacity at steep prices, but with no assurance of dependability [(Domanal and Reddy, 2018)].

4. **Plans for savings:** Some cloud providers provide volume discounts based on the overall amount of cloud services ordered by an enterprise.

What cost factors make Nife the best cloud computing platform?#

The cost factors which make Nife the best cloud computing platform are:

• Utilization of computes instances — with prices variable depending on the instance type and pricing strategy.

• Utilization of cloud storage services — with varying costs depending on the service, storage tier, storage space consumed, and data activities done.

• Database services are commonly used to run managed databases on the cloud, with costs for compute instances, storage, and the service itself [(Changchit and Chuchuen, 2016)].

• Most cloud providers charge for inbound and outgoing network traffic.

• Software licensing – even if the cost of a managed service is included in the per-hour price, the software still has a cost in the cloud.

• Support and consultancy – In addition to paying for support, the best cloud computing platforms may require extra professional services to implement and manage their cloud systems.

What are Nife's Cost Saving Strategies that make it the best cloud computing services provider?#

Here is the list of cost factors making NIFE the best cloud computing services provider:

Workload schedules

Schedules can be set to start and stop based on the needs of the task. There is no point to activate and pay for a resource if no one is utilising it.

Make use of Reserved Instances.

Businesses considering long-term cloud computing investments might consider reserved instances. Cloud companies such as NIFE offer savings of up to 75% for pledging to utilise cloud resources in advance.

Utilize Spot Instances

Spot instances have the potential to save much more than allocated instances. Spot instances are a spare capacity that is sold at a discount by the cloud provider [(Okita et al., 2018)]. They are back on the market and can be acquired at a discount of up to 90%.

Utilize Automation

Use cloud automation to deploy, set up, and administer Nife's best cloud computing services wherever possible. Automation operations like backup and storage, confidentiality and availability, software deployment, and configuration reduce the need for manual intervention. This lowers human mistakes and frees up IT employees to focus on more critical business operations.

Automation has two effects on cloud costs:

1. You obtain central control by automating activity. You may pick which resources to deploy and when at the department or enterprise level.

2. Automation also allows you to adjust capacity to meet current demand. Cloud providers give extensive features for sensing application load and usage and automatically scaling resources based on this data.

Keep track of storage use.

The basic cost of cloud storage services is determined by the storage volumes provisioned or consumed. Users often close projects or programs without removing the data storage. This not only wastes money but also raises worries about security. If data is rarely accessed but must be kept for compliance or analytics, it might be moved to archive storage.

Cloud Cost Management refers to the idea of effectively controlling your cloud expenditures. It typically entails evaluating your cloud's expenses and reducing those that are unneeded in the best cloud computing platforms. There are no shortcuts when it comes to expense management. Make solid planning, get the fundamentals right, and include your teams so they realize the gravity of the problem. Cloud cost management has emerged as a critical subject for cloud computing technology and Multi-Access Edge Computing, as well as a new need for every software firm.

Cloud Cost Management Tools Used in the Best Cloud Computing Platforms#

Cloud Cost Optimization: Organizations frequently overspend with their cloud service providers and want to reduce expenses so that they only pay for whatever they need. They must reduce cloud-related expenses.

Transparency in Cloud Expenses: Cloud costs should be visible at all levels of the company, from executives to engineers. All participants must be able to grasp cloud costs in their situation.

Cloud Cost Governance: Guardrails should be put in place regarding cloud computing technologies expenses, basically building systems to guarantee costs are kept under control.

Best Practices for Cloud Cost Management#

You may apply the best practices for cloud cost management given below to create a cloud cost optimization plan that relates expenses to particular business activities such as Multi-Access Edge Computing and Cloud Computing Technology, allowing you to identify who, what, why, and how your cloud money would be spent.

Underutilized Resources Should Be Rightsized or Resized

Making sure your clusters are properly scaled is one of the most effective methods to cut costs on your cloud infrastructure. Implementing tips may assist you in optimizing costs and lowering your cloud expenditures. It can also suggest improvements to instance families. Continuous variables do more than just lower cloud expenses; it also assists in cloud optimization or making the most of the services you pay for.

Unused Resources Should Be Shut Down

A cloud management platform/tool may detect idle, unallocated, and underused virtual machines/resources. Idle resources are ones that were formerly operational but are now turned off, raising expenditures. Purchased but never used unallocated or underused virtual machines (VMs) [(Adhikari and Patil, 2013)]. You spend for what you order or buy, not what you utilize with any cloud platform.

Setup AutoStopping Rules

AutoStopping Rules are a strong and dynamic resource orchestrator for non-production demands. Some of the major benefits of implementing AutoStopping Rules into your cloud services are as follows:

• Detect idle moments automatically and shut down (on-demand) or terminate (spot) services.
• Allow workloads to execute on fully coordinated checks for signs while stressing over spot disruptions.
• Calculate idle times, especially throughout the working time.
• Stop cloud services without optimizing compute; just start/stop operations are supported.

Detect Cloud Cost Inconsistencies

A technique for detecting cloud cost anomalies in the best cloud computing platforms can be used to keep cloud expenses under control. Cost anomaly detection indicates what you should be looking for to keep your cloud expenses under control (save money). An alert is generated if your cloud costs significantly increase. This assists you in keeping track of potential waste and unanticipated expenditures. It also records repeating occurrences (seasonality) that occur on a daily, weekly, or monthly basis.

Set a Fixed Schedule for Uptime or Downtime

Configure your resources' uptime and downtime schedules. For that duration, you can set downtime for the specified resources. Your selected services will be unavailable during this time, allowing you to save money. This is especially useful when many teams are using/using the same resources as in Multi-Access Edge Computing.

Create Budgets and Thresholds for Teams and Projects

Make your budgets and get reminders when your expenses surpass (or are projected to exceed) your budget. You can also specify a budget percentage barrier based on actual or expected costs. Setting budgets and boundaries for various teams and business units can help to reduce cloud waste significantly.

Establish a Cloud Center of Excellence Team

A Cloud Center of Excellence (CCoE) is comprised of executives (CFO and CTO), an IT Manager, an Operations Manager, a System Architect, an Application Developer, a Network Engineer, and a Database Engineer [(AlKadi et al., 2019)]. This group may assist you in identifying opportunities for cloud cost minimization.

"Cost Impact of Cloud Computing Technology" Culture#

Every important feature should have a Cloud Cost Impact checkbox. This promotes a mindset and attitude among application developers and the cross-functional team that expenses are just another boundary condition to be optimized over time and make your platform the best cloud computing platform.

Conclusion#

Consider how your company is now working in the cloud. Is your company's Cloud Operating Model well-defined? Is your company using the best cloud computing platforms? Are you using Multi-Access Edge Computing? Cloud cost management does not have to be difficult, but it does need a disciplined strategy that instills strong rightsizing behaviors and consistently drives insights and action through analytics to reduce your cloud bill. And here is where Nife's cloud computing technology shines.

5G facilitates the development of new business models in all industries. Even now, network slicing will play a critical role in enabling service providers can offer innovative products and services to access new markets and develop their companies. Network slicing is the process of layering numerous virtual networking on the pinnacle of a common network domain, which is a collection of network connections and computational resources. Cloud computing services and Slicing networks allow network operators to enhance network resource utilization and wide scope.

What is network slicing?#

Network slicing is the carriers' best response for building and managing a network that matches and surpasses the evolving needs of a diverse set of users. A sliced network is created by transforming it into a collection of logical networks built on top of shared infrastructure. Each conceptual network is developed to match a specific business function and includes all of the necessary network resources that are configured and linked end-to-end.

Cloud computing services and Network Slicing#

Cloud computing services along with Network Slicing enable new services by combining network and cloud technologies. Cloud Network Slicing is the process of creating discrete end-to-end and on-demand networking abstractions that comprise both Cloud computing services and network services that can be managed, maintained and coordinated separately. Technological advances that potentially benefit from cloud network slicing include critical communications, V2X, Massive IoT, and eMBB (enhanced Mobile Broadband). Distinct services have different needs, such as extremely high throughput, high connection density, or ultra-low latency. According to the established SLA, these must be able to accommodate services with different features.

In Content Delivery Network#

Content delivery network slicing was developed to handle large amounts of content and long-distance transmissions. Content delivery network slicing as a Service (CDNaaS) technology can build virtual machines (VMs) across a network of data centres and give consumers a customised slice of the content delivery network slicing. Caches, transcoders, and streamers deployed in multiple VMs let CDNaaS manage a large number of movies. To produce an efficient slice of Content delivery networks, however, an ideal arrangement of VMs with appropriate flavours for the various pictures is necessary.

5G Edge Network Slicing#

Distinguishable offerings with assured quality of service to varied clients are enabled by 5G Edge network slicing across the shared network infrastructure. It is an end-to-end solution that operates across the Radio Access Network (RAN), the transport layer, the Core network, and the enterprise cloud.

5G service types#

The following 5G service types are the high-level types of network slice architecture which employ slicing for differentiated traffic handling:

Enhanced Mobile Broadband- delivers cellular access to data in three ways: too dense groups of users, extremely maneuverable users, and consumers scattered across large regions. It is based on characteristics such as massive networks of numerous entries, multiple-output (MIMO) transmitters and the mixing of bands beginning with standard 4G wavelengths and reaching into the millimetre band [(Kourtis et al., 2020)].

Massive Machine-Type Communications - services designed to serve a wide range of devices in a compact area while generating little data (tens of bytes/sec) and withstanding significant latency (up to 10 seconds on a round trip). Furthermore, the requirements mandate that data transmission and reception consume little energy so that gadgets can have great battery lifetimes

Ultra-reliable low-latency communications - 5G Edge network is used to provide encrypted systems with latencies of 1 millisecond (ms) and great dependability with minimal, or perhaps even nil, transmission errors. Hardware optimization on MIMO antenna arrays, concurrent manipulation of several bandwidths, package encoding and computing methods, and efficient signal handling is used to achieve this.

Advantages#

Slicing, in conjunction with virtual network activities, is the key to "just right" services for service providers. More capacity to modify affordably gives the following benefits to service providers:

• Reduce the obstacles to testing out new service offers to create new income prospects.

• Increase flexibility by allowing additional types of services to be supplied concurrently because they do not require dedicated or specialised hardware.

• Because all of the physical infrastructures are generic, easier scalability is feasible.

• Better return on the investment is also possible since the capacity to continually test new things allows for the most efficient use of resources.

Conclusion#

As the 5G edge network, Cloud computing services and Content delivery network introduces new technology and opens up new business potential in many industries, businesses are searching for creative solutions to fulfil their demands and capitalize on new chances. Enterprise users expect automated business and operational procedures that begin with buying the service and continue through activation, delivery, and decommissioning. They want services to be delivered quickly and securely. Communication service providers may satisfy all of their corporate clients' demands by slicing their networks.

Introduction#

Organizations are constantly concentrating on lowering network latency and computing delay duration, as well as the volume of data communicated or maintained in the server. Organizations recognise the need to modify their processing practices and are adopting Edge Computing to speed their Digitalization activities [(Dokuchaev, 2020)]. The job of digital transformation is primarily reliant on data processing. However, to make substantial modifications, organisations must frequently make major changes as far as how data is being collected, handled, and analysed. As organizational edge computing apps acquire traction, it is increasingly evident how much they will interact with digitalization programmes. Edge computing might be the connection that amplifies prospective corporate goals in the form of different continuous innovations, such as deep learning or the Internet of things.

Traditional cloud Vs. Edge Computing#

The traditional cloud-based model relies on a centralized database, where data is obtained on the periphery and then transported to the main data centres for analysis. Edge computing negates such a need to send raw information to the central network infrastructure. It implements a decentralized IT infrastructure in which data is processed near the edge, in which it is created and absorbed and it also empowers more instantaneous impact of analysis tools and AI functionality.

Edge Computing's Role in Digital Transformation

Edge Computing's role in Digital Business transformation could indeed allow rapid, less constrictive data processing, allowing for additional insight, quicker reaction times, and enhanced client interactions. Edge and AI-powered products and AI can instantly comprehend, understand, and make decisions and Data processes. Edge Computing on Internet of things devices can significantly decrease delay, boost performance, and enable enhanced decisions, laying the groundwork for simplified IT facilities. Furthermore, the coming of 5G technology, paired with both the potential of Edge Computing and IoT, has the potential to provide endless future opportunities.

Edge Computing's Digital Transformation across various business#

Manufacturing & Operations

Edge computing enables improved preventative analysis, improves efficiency, and energy usage, and improves dependability and effective availability in industrial enterprises [(Albukhitan, 2020)]. Edge Computing may assist businesses in making quicker and more effective marketing choices about their operational functions. Edge computing may be extremely advantageous for manufacturers engaged in places with limited or non-existent broadband.

Distribution Network

A lot of things happen along the distribution chain's edge, and a much may go incorrect. Businesses may extend the accessibility and exposure of their distribution networks by separating activities into groups of lesser, relatively controllable activities by digitally linking and managing the operations at the edge. The information gained from the edges of distribution networks, supported by AI and computerized technologies, would assist businesses to efficiently respond to market circumstances, foresee lengthy patterns ahead of their rivals, and adapt plans at the moment down to its regional scale [(Ganapathy, 2021)].

Workplace security#

Edge computing has the potential to improve safety regulations across enterprises. The said Edge technology could indeed integrate and interpret information from on-site camera systems, worker security devices, and numerous other detectors to assist businesses in keeping tabs on employment conditions or make sure that all staff have significant compliance safety procedures, particularly when a place of work is distant or exceptionally risky [(Atieh, 2021)].

Autonomous Vehicles#

To function properly, autonomous cars will have to collect and evaluate massive volumes of data about their settings, routes, weather patterns, communicating with several other on-road automobiles, and so forth [(Liu et al., 2019)]. Edge Computing will allow self-driving cars to gather, analyse, and distribute information in real-time across automobiles and larger networks.

Retail#

Edge Computing may assist retail enterprises in maximising the usage of IoT devices and transmitting a multitude of data in real-time including monitoring, inventory management, retail sales, and so on [(Ganapathy, 2021)]. This innovation may be used to fuel Artificial intelligence and machine learning technologies, and also uncover commercial possibilities such as an efficient endcap or promotion, anticipate sales, optimise supplier procurement, and so forth.

Healthcare#

The healthcare business has seen an exponential increase in the amount of client data collected by gadgets, monitors, as well as other medical devices. Edge Computing enables organisations to gain access to data, particularly issue data, so that professionals may take quick action to assist patients to prevent health crises instantaneously (Hartmann, Hashmi and Imran, 2019).

Conclusion#

Since Edge Computing has yet to see widespread acceptance, the potential of this digitalization cannot be underestimated. Edge Computing, being the most practical infrastructure for placing computing infrastructure directly to the data source, may help organisations accelerate their Digital Transformation emphasis. The edge technology's importance will be seen broadly soon because it can successfully handle developing network difficulties connected with transporting massive amounts of data that enterprises create and consume today. It is no longer only an issue of quantity, but also of latencies because apps rely on analysis and reactions that are more time-sensitive.

Introduction#

Organizations are constantly concentrating on lowering network latency and computing delay duration, as well as the volume of data communicated or maintained in the server. Organizations recognise the need to modify their processing practices and are adopting Edge Computing to speed their Digitalization activities [(Dokuchaev, 2020)]. The job of digital transformation is primarily reliant on data processing. However, to make substantial modifications, organisations must frequently make major changes as far as how data is being collected, handled, and analysed. As organizational edge computing apps acquire traction, it is increasingly evident how much they will interact with digitalization programmes. Edge computing might be the connection that amplifies prospective corporate goals in the form of different continuous innovations, such as deep learning or the Internet of things.

Traditional cloud Vs. Edge Computing#

The traditional cloud-based model relies on a centralized database, where data is obtained on the periphery and then transported to the main data centres for analysis. Edge computing negates such a need to send raw information to the central network infrastructure. It implements a decentralized IT infrastructure in which data is processed near the edge, in which it is created and absorbed and it also empowers more instantaneous impact of analysis tools and AI functionality.

Edge Computing's Role in Digital Transformation

Edge Computing's role in Digital Business transformation could indeed allow rapid, less constrictive data processing, allowing for additional insight, quicker reaction times, and enhanced client interactions. Edge and AI-powered products and AI can instantly comprehend, understand, and make decisions and Data processes. Edge Computing on Internet of things devices can significantly decrease delay, boost performance, and enable enhanced decisions, laying the groundwork for simplified IT facilities. Furthermore, the coming of 5G technology, paired with both the potential of Edge Computing and IoT, has the potential to provide endless future opportunities.

Edge Computing's Digital Transformation across various business#

Manufacturing & Operations

Edge computing enables improved preventative analysis, improves efficiency, and energy usage, and improves dependability and effective availability in industrial enterprises [(Albukhitan, 2020)]. Edge Computing may assist businesses in making quicker and more effective marketing choices about their operational functions. Edge computing may be extremely advantageous for manufacturers engaged in places with limited or non-existent broadband.

Distribution Network

A lot of things happen along the distribution chain's edge, and a much may go incorrect. Businesses may extend the accessibility and exposure of their distribution networks by separating activities into groups of lesser, relatively controllable activities by digitally linking and managing the operations at the edge. The information gained from the edges of distribution networks, supported by AI and computerized technologies, would assist businesses to efficiently respond to market circumstances, foresee lengthy patterns ahead of their rivals, and adapt plans at the moment down to its regional scale [(Ganapathy, 2021)].

Workplace security#

Edge computing has the potential to improve safety regulations across enterprises. The said Edge technology could indeed integrate and interpret information from on-site camera systems, worker security devices, and numerous other detectors to assist businesses in keeping tabs on employment conditions or make sure that all staff have significant compliance safety procedures, particularly when a place of work is distant or exceptionally risky [(Atieh, 2021)].

Autonomous Vehicles#

To function properly, autonomous cars will have to collect and evaluate massive volumes of data about their settings, routes, weather patterns, communicating with several other on-road automobiles, and so forth [(Liu et al., 2019)]. Edge Computing will allow self-driving cars to gather, analyse, and distribute information in real-time across automobiles and larger networks.

Retail#

Edge Computing may assist retail enterprises in maximising the usage of IoT devices and transmitting a multitude of data in real-time including monitoring, inventory management, retail sales, and so on [(Ganapathy, 2021)]. This innovation may be used to fuel Artificial intelligence and machine learning technologies, and also uncover commercial possibilities such as an efficient endcap or promotion, anticipate sales, optimise supplier procurement, and so forth.

Healthcare#

The healthcare business has seen an exponential increase in the amount of client data collected by gadgets, monitors, as well as other medical devices. Edge Computing enables organisations to gain access to data, particularly issue data, so that professionals may take quick action to assist patients to prevent health crises instantaneously (Hartmann, Hashmi and Imran, 2019).

Conclusion#

Since Edge Computing has yet to see widespread acceptance, the potential of this digitalization cannot be underestimated. Edge Computing, being the most practical infrastructure for placing computing infrastructure directly to the data source, may help organisations accelerate their Digital Transformation emphasis. The edge technology's importance will be seen broadly soon because it can successfully handle developing network difficulties connected with transporting massive amounts of data that enterprises create and consume today. It is no longer only an issue of quantity, but also of latencies because apps rely on analysis and reactions that are more time-sensitive.

Introduction#

The notion of 'Interconnection Oriented Architecture (IOA)' might feel complex for a network system, but it's a simpler approach that relies on what's previously established inside the technological base. IOA, or Interconnection Oriented Architecture, is a corporate network approach that uses WAN, LAN, and cloud technology to accelerate data transport over long distances whilst maintaining clients' security and compatibility in every way [(Chadha, 2018)]. It supports a dynamic corporate network that increases digital participation and digital transition throughout the organisation.

Function of IOA

An IOA exists at the crossroads of virtual and [physical networking] systems; it connects Lan and WAN networks to enable digital interaction at any site and place in your organisation. IOA is capable of creating a value approach that fits your company's demands and therefore can respond more swiftly in the future by leveraging most of your current communications infrastructure. Interconnection Architecture is a multi-layered networking strategy that consists of a linked set of nodes, each having its type of communication [(Wrabetz and Weaver, 2018)]. The edge nodes are constructed from four separate layers:

What distinguishes IOA from SOA?#

As a technologically knowledgeable professional, you've certainly heard of business architecture concepts such as SOA. But what distinguishes SOA from IOA?

SOA is a software design method in which elements get resources via networking connectivity protocols. It is similar to a self BlackBox for a certain functional area. SOA in action is demonstrated through REST or representational data transfer [(Kasparick et al., 2018)]. Whereas IOA is an infrastructure development method based on Equinix colocation hub deployments. It addresses various key business issues that affect information and communication distribution.

Importance of IOA to businesses#

Networking is essential for effective corporate administration and consumer interaction. But why is IOA superior or more significant for your organisation than some other network technologies?

In reality, IOA solves numerous essential business difficulties and offers some unique advantages that can assist your company in adapting to and embracing the digital revolution. IOA connectivity depends on the location of the user instead of the network operator; it is vastly more extensible and adaptable than typical legacy networks [(Chauhan et al., 2019)]. This is a novel approach to addressing frequent business concerns or issues confronting your IT team.

Advantages of 'Interconnection-Oriented Architecture (IOA)#

Solving essential business challenges using IOA provides your company with a slew of new advantages, ranging from reduced latency to higher efficiency and everything else in between [(Sony, 2017)]. Whereas the advantages of IOA do not stop there, and here's a greater glance at what you may anticipate upon making the shift:

What kind of enterprises should use IOA?#

Whilst the IOA is useful to any business, it is most suitable for scattered or distant companies with an in-house IT team and a distinct requirement for enterprise-wide digital communication. These businesses frequently have a large amount of critical data flowing backward and forward over the networks, necessitating greater information leak prevention and GDPR conformity [(Stocker et al., 2017)]. In reality, the deployment of an IOA will assist all departments in the organization:

Sales Division - Interact with local sales staff and interact with potential customers throughout the engagement.

Marketing Division - Coordination of marketing and material distribution with the in-site and offsite personnel in various regions.

IT Division - Ease location configuration and activation while easily managing and maintaining data security and integrity throughout the enterprise.

Human Capital - Worker conformity with regulatory authorities should be evaluated and maintained.

C-Suite - Receive real-time information and views into key performance indicators (KPIs) to communicate with shareholders and investors [(Demchenko et al., 2015)].

How does IOA fit within your current infrastructure?#

IOA integrates LAN and WAN for improved networking connectivity; therefore, whether you're currently utilising SD-WAN or hybrid cloud computing services, it is an incredible asset to your infrastructure. IOA may be pushed out one area at a time to guarantee ease of acceptance and to track and handle any problems in the process, simplifying your deployment plan.

Conclusion#

Your company's connection is critical. Obtaining the best of all worlds is not a huge burden for your IT staff, and neither would it have to mean sacrificing business solutions. Return control over the business connectivity to your IT team by going ahead with an IOA.

Introduction#

Edge computing, which is appropriate for serverless apps and other new ways of computing, is becoming more popular among developers. Edge computation moves development services and data directly to end-users by locating the computing functionality on the network's perimeter instead of in a centralised centre [(Cao et al., 2020)]. Many businesses have centralised their operations within massive data centres as a result of cloud technology. But, emerging end-user experiences, such as the Internet of Things (IoT), necessitate service delivery nearer to the network's "edges," where actual objects reside.

What is Edge Compute?#

Edge computing is the process of operating programs at the network's edge instead of on centralised equipment in a data centre or the cloud [(Premsankar, Di Francesco and Taleb, 2018)]. Nowadays, this implies virtualized computing, while various kinds of edge computing have existed in the past. The word also encompasses the whole set of technology, resources, and procedures that enable the capacity. This involves having an edge runtime environment, a programmer platform that is aligned with edge computing, an edge code deployment method, and so on.

What is an Edge device?#

Edge devices are pieces of physical machine positioned at the network's edge that have sufficient storage, processing capabilities, and computational capabilities to gather data, analyse it, and operate on it in near real-time with only little assistance from other sections of the network [(Gomes et al., 2015)]. Edge devices required network access to enable back-and-forth connectivity between both the machine and a centralised server. The data is gathered and analysed at the edge device.

When is Edge Computing useful?#

Edge computing is an attractive computing solution for a wide range of applications. It is not, though, a substitute for data centres or the cloud. Instead, the edge is indeed an extra location for code to execute. When target customers could gain through edge computing, it represents the largest value. For several reasons, developers seek to place computing near the edge when an online platform demands the lowest feasible amount of delay, and executing application programs closer to the people will achieve this aim [(Satyanarayanan, 2017)].

What are the typical use cases of edge computing?#

Edge computing can supplement a hybrid computing architecture in situations where centralised computing is employed, such as:

• Computation-intensive workloads
• Data collection and storage
• Machine learning/artificial intelligence
• Vehicles that drive themselves
• Augmented and Virtual Reality
• Smart Cities

Edge computing could also aid in the resolution of issues at the source of data in real-time. In general, there is indeed a use case for edge computing if decreased delay and/or real-time surveillance can serve business objectives.

The Internet of Things (IoT) - There may be several network stages in getting and processing a response for an IoT device. The greater the computing capability accessible on the machine physically, or near this in the network, the greater the customer experience.

5G - 5G is a use case for edge computing that also supports additional edge use cases.

Mobile technologies - When concerns develop in mobile computing, issues frequently focus on delay and disruption of services. By lowering data transmission delays, it can assist solve for strict latency limitations.

Telecommunications - As network operators update their networks, workload, and operations are being moved from the network infrastructure (datacentres) to the network's edge: surrounding stations and main locations [(Moura and Hutchison, 2019)].

What are the benefits of Edge Compute?#

Edge computing has several benefits for programmers and developers. The key beneficial effect, which leads to better end-user experiences, is low latency, although it is far from the only one. Putting computation at the edge promotes innovation. It moves to control and trust choices to the edge, allowing for more real-time apps and experiences with little personal data transit. Edge computing allows programmers to "simply code" without having to handle the difficulties of procuring computing resources and distributing code just at the edge with the correct tooling [(Cao et al., 2020)].

Why do IoT and edge computing have to collaborate?#

IoT generates a tremendous volume of data, which must be handled and evaluated before use. Edge computing brings computer resources closer to the edge or source of data, including an IoT system. Edge computing is indeed a localized resource of storage and processing for IoT device information and processing requirements, reducing communication latency between IoT systems and the main IT network to which they are linked [(Ai, Peng and Zhang, 2018)].

Final Thought#

Edge computing is a valuable resource and technique in today's data centre. Many telecommunication businesses are prioritizing edge as they update their network and explore new revenue streams. Many network operators, in particular, are shifting workloads and services out from the network infrastructure (in cloud data centres) and toward the network's edge, to global locations and main offices.

Everyone is now heading to the Cloud World (AWS, GCP, Azure, PCF, VMC). A public cloud, a private cloud, or a hybrid cloud might be used. These cloud computing services offer on-demand computing capabilities to meet the demands of consumers. They provide options by keeping IT infrastructure open, from data to apps. The field of cloud-based services is wide, with several models. It might be difficult to sort through the abbreviations and comprehend the differences between the many sorts of services (Rajiv Chopra, 2018). New versions of cloud-based services emerge as technology advances. No two operations are alike, but they do have some qualities. Most crucially, they simultaneously exist in the very same space, available for individuals to use.

Infrastructure as a Service (IaaS)#

IaaS offers only a core infrastructure (VM, Application Define Connection, Backup connected). End-users must set up and administer the platform and environment, as well as deploy applications on it (Van et al., 2015).

Examples - Microsoft Azure (VM), AWS (EC2), Rackspace Technology, Digital Ocean Droplets, and GCP (CE)

Advantages of IaaS

• Decreasing the periodic maintenance for on-premise data centers.

• Hardware and setup expenditures are eliminated.

• Releasing resources to aid in scaling

• Accelerating the delivery of new apps and improving application performance

• Enhancing the core infrastructure's dependability.

• IaaS providers are responsible for infrastructure maintenance and troubleshooting.

During service failures, IaaS makes it simpler to access data or apps. Security is superior to in-house infrastructure choices.

Container as a Service (CaaS)#

CaaS is a type of container-based virtualization wherein customers receive container engines, management, and fundamental computing resources as a service from the cloud service provider (Smirnova et al., 2020).

Examples - are AWS (ECS), Pivotal (PKS), Google Container Engine (GKE), and Azure (ACS).

Advantages of CaaS

• Containerized applications have all the necessary to operate.

• Containers can accomplish all that VM could without the additional resource strain.

• Containers need lower requirements and do not require a separate OS.

• Containers are maintained isolated from each other despite both having the very same capabilities.

• The procedure of building and removing containers is rapid. This speeds up development or operations and reduces time to market.

Platform-as-a-Service (PaaS)#

It offers a framework for end-users to design, operate, and administer applications without having to worry about the complexities of developing and managing infrastructure (Singh et al., 2016).

Examples - Google App Engine, AWS (Beanstalk), Heroku, and CloudFoundry.

Advantages of PaaS

• Achieve a competitive edge by bringing their products to the marketplace sooner.

• Create and administer application programming interfaces (APIs).

• Data mining and analysis for business analytics

• A database is used to store, maintain, and administer information in a business.

• Build frameworks for creating bespoke cloud-based applications.

• Put new languages, OS, and database systems into the trial.

• Reduce programming time for platform tasks such as security.

Function as a Service (FaaS)#

FaaS offers a framework for clients to design, operate, and manage application features without having to worry about the complexities of developing and managing infrastructure (Rajan, 2020).

Examples - AWS (Lamda), IBM Cloud Functions, and Google Cloud Function

Advantages of FaaS

• Businesses can save money on upfront hardware and OS expenditures by using a pay-as-you-go strategy.

• As cloud providers deliver on-demand services, FaaS provides growth potential.

• FaaS platforms are simple to use and comprehend. You don't have to be a cloud specialist to achieve your goals.

• The FaaS paradigm makes it simple to update apps and add new features.

• FaaS infrastructure is already highly optimized.

Software as a Service (SaaS)#

SaaS is also known as "on-demand software" at times. Customers connect a thin client using a web browser (Sether, 2016). Vendors may handle everything in SaaS, including apps, services, information, interfaces, operating systems, virtualisation, servers, storage, and communication. End-users must utilize it.

Examples - Gmail, Adobe, MailChimp, Dropbox, and Slack.

Advantages of SaaS

• SaaS simplifies bug fixes and automates upgrades, relieving the pressure on in-house IT workers.

• Upgrades pose less risk to customers and have lower adoption costs.

• Users may launch applications without worrying about managing software or application. This reduces hardware and license expenses.

• Businesses can use APIs to combine SaaS apps with other software.

• SaaS providers are in charge of the app's security, performance, and availability to consumers.

• Users may modify their SaaS solutions to their organizational processes without having any impact according to their infrastructures.

Conclusion for Cloud Computing Services#

Cloud services provide several options for enterprises in various industries. And each of the main — PaaS, CaaS, FaaS, SaaS, and IaaS – has advantages and disadvantages. These services are available on a pay-as-you-go arrangement through the Internet. Rather than purchasing the software or even other computational resources, users rent them from a cloud computing solution (Rajiv Chopra, 2018). Cloud services provide the advantages of sophisticated IT infrastructure without the responsibility of ownership. Users pay, users gain access, and users utilise. It's as easy as that.

Everyone is now heading to the Cloud World (AWS, GCP, Azure, PCF, VMC). A public cloud, a private cloud, or a hybrid cloud might be used. These cloud computing services offer on-demand computing capabilities to meet the demands of consumers. They provide options by keeping IT infrastructure open, from data to apps. The field of cloud-based services is wide, with several models. It might be difficult to sort through the abbreviations and comprehend the differences between the many sorts of services (Rajiv Chopra, 2018). New versions of cloud-based services emerge as technology advances. No two operations are alike, but they do have some qualities. Most crucially, they simultaneously exist in the very same space, available for individuals to use.

Infrastructure as a Service (IaaS)#

IaaS offers only a core infrastructure (VM, Application Define Connection, Backup connected). End-users must set up and administer the platform and environment, as well as deploy applications on it (Van et al., 2015).

Examples - Microsoft Azure (VM), AWS (EC2), Rackspace Technology, Digital Ocean Droplets, and GCP (CE)

Advantages of IaaS

• Decreasing the periodic maintenance for on-premise data centers.

• Hardware and setup expenditures are eliminated.

• Releasing resources to aid in scaling

• Accelerating the delivery of new apps and improving application performance

• Enhancing the core infrastructure's dependability.

• IaaS providers are responsible for infrastructure maintenance and troubleshooting.

During service failures, IaaS makes it simpler to access data or apps. Security is superior to in-house infrastructure choices.

Container as a Service (CaaS)#

CaaS is a type of container-based virtualization wherein customers receive container engines, management, and fundamental computing resources as a service from the cloud service provider (Smirnova et al., 2020).

Examples - are AWS (ECS), Pivotal (PKS), Google Container Engine (GKE), and Azure (ACS).

Advantages of CaaS

• Containerized applications have all the necessary to operate.

• Containers can accomplish all that VM could without the additional resource strain.

• Containers need lower requirements and do not require a separate OS.

• Containers are maintained isolated from each other despite both having the very same capabilities.

• The procedure of building and removing containers is rapid. This speeds up development or operations and reduces time to market.

Platform-as-a-Service (PaaS)#

It offers a framework for end-users to design, operate, and administer applications without having to worry about the complexities of developing and managing infrastructure (Singh et al., 2016).

Examples - Google App Engine, AWS (Beanstalk), Heroku, and CloudFoundry.

Advantages of PaaS

• Achieve a competitive edge by bringing their products to the marketplace sooner.

• Create and administer application programming interfaces (APIs).

• Data mining and analysis for business analytics

• A database is used to store, maintain, and administer information in a business.

• Build frameworks for creating bespoke cloud-based applications.

• Put new languages, OS, and database systems into the trial.

• Reduce programming time for platform tasks such as security.

Function as a Service (FaaS)#

FaaS offers a framework for clients to design, operate, and manage application features without having to worry about the complexities of developing and managing infrastructure (Rajan, 2020).

Examples - AWS (Lamda), IBM Cloud Functions, and Google Cloud Function

Advantages of FaaS

• Businesses can save money on upfront hardware and OS expenditures by using a pay-as-you-go strategy.

• As cloud providers deliver on-demand services, FaaS provides growth potential.

• FaaS platforms are simple to use and comprehend. You don't have to be a cloud specialist to achieve your goals.

• The FaaS paradigm makes it simple to update apps and add new features.

• FaaS infrastructure is already highly optimized.

Software as a Service (SaaS)#

SaaS is also known as "on-demand software" at times. Customers connect a thin client using a web browser (Sether, 2016). Vendors may handle everything in SaaS, including apps, services, information, interfaces, operating systems, virtualisation, servers, storage, and communication. End-users must utilize it.

Examples - Gmail, Adobe, MailChimp, Dropbox, and Slack.

Advantages of SaaS

• SaaS simplifies bug fixes and automates upgrades, relieving the pressure on in-house IT workers.

• Upgrades pose less risk to customers and have lower adoption costs.

• Users may launch applications without worrying about managing software or application. This reduces hardware and license expenses.

• Businesses can use APIs to combine SaaS apps with other software.

• SaaS providers are in charge of the app's security, performance, and availability to consumers.

• Users may modify their SaaS solutions to their organizational processes without having any impact according to their infrastructures.

Conclusion for Cloud Computing Services#

Cloud services provide several options for enterprises in various industries. And each of the main — PaaS, CaaS, FaaS, SaaS, and IaaS – has advantages and disadvantages. These services are available on a pay-as-you-go arrangement through the Internet. Rather than purchasing the software or even other computational resources, users rent them from a cloud computing solution (Rajiv Chopra, 2018). Cloud services provide the advantages of sophisticated IT infrastructure without the responsibility of ownership. Users pay, users gain access, and users utilise. It's as easy as that.

Containers are a type of virtualization for operating systems. A solitary container may host everything from little services or programming activities to a huge app. All compiled code, binary data, frameworks, and application settings are contained within a container. Containers, in contrast to host or device virtualization techniques, do not include OS copies. As a result, they are lighter and much more mobile, with significantly less expense. Several containers could well be installed for one or much more container groups in bigger commonly used software (Hussein, Mousa and Alqarni, 2019). A container scheduler, such as Kubernetes, may handle such groups.

Container as a Service (CaaS)#

Containers as a Service (CaaS) is a cloud-based option that enables app developers and IT organizations to use container-based virtualization to load, organize, execute, manage, and control containers. CaaS primarily refers to the automatic management and installation of container development tools. In CaaS, developers must install, operate, and manage the hardware on which containers operate. This architecture is a combination of cloud computers and networking traffic devices that must be overseen and managed by specialized DevOps employees.

CaaS allows developers to operate at the upper-level container stage rather than getting bogged down by lesser hardware maintenance [(Piraghaj et al., 2015)]. This provides a developer with greater clarity on the ultimate product, allowing for even more flexible performance and increased consumer experience.

CaaS Features and Benefits for DevOps#

CaaS solutions are used by companies and DevOps teams to:

• Increase the speed of software development.
• Develop creative cloud services at scale.

SDLC teams may deliver software platforms quicker while lowering the expenses, inefficiencies, and wasteful procedures that are common in technology design and delivery.

The benefits of CaaS are-

• CaaS enables it simpler to install and build application software, as well as to construct smaller services.
• Throughout programming, a container accumulation might manage various duties or programming settings (I Putu Agus Eka Pratama, 2021).
• A container network partnership is defined and bound to transport.
• CaaS guarantees that such specified and specialized container architectures may be swiftly installed in cloud computing.
• Assume a fictitious software system created using a microservice model, where the operation design is organized with a specific industry ID. Transactions, identification, and a checkout process are examples of service areas.
• Such software containers could be immediately deployed to a real-time framework using CaaS.
• Uploading programs deployed on the CaaS system allows program effectiveness via the use of tools like data incorporation and analysis.
• CaaS also incorporates automatic monitoring efficiency and coordination control integrated into.
• It helps team members to rapidly create clear views and decentralized applications for high reliability.
• Furthermore, CaaS empowers developers by providing quicker installation.
• Containers are utilized to avoid focused distribution; however, CaaS can save technical running expenses by lowering the number of DevOps people required to handle installations [(Saleh and Mashaly, 2019)].

Container as a Service (CaaS) drawbacks:

• The tech provided differs based on the supplier.
• It is risky to extract corporate information from the cloud.

CaaS Security Concerns#

• Containers are regarded as better than Windows Processes, although they do pose certain hazards.
• Containers, despite being easily configurable, use a very similar kernel to that of the OS.
• If indeed the containers are attacked, they are all in danger of becoming attacked [(Miller, Siems and Debroy, 2021)].
• When containers are installed within Cloud through CaaS, the hazards multiply dramatically.

Performance Restrictions#

• Containers are obvious sections that do not operate on physical hardware.
• Something is lacking with the additional level between both the physical hardware and the program containers and their contents [(Liagkou et al., 2021)].
• When these are combined with the container's system failures connected with the dedicated server, the consequence is a considerable decrease in performance.
• As a result, even with elevated equipment, enterprises must expect a significant decrease in container performance.

How Does CaaS Work?#

A container as just a service is a digital cloud that can be accessed and computed. Customers utilize the cloud infrastructure to distribute, build, maintain, and execute container-based apps. A GUI and API requests could be used to communicate with a cloud-based system (Zhang et al., 2019). The core of the CaaS system is an administration feature that allows complicated container architectures to be managed. Instrumentation technologies connect running containers and allow for automatic actions. The CaaS platform's current operator has a powerful effect on the services supplied by customers.

Why is CaaS Important?#

• Assists programmers in developing fully scalable containers and configuration management.
• It aids in the simplification of container management.
• Aids in the automation of essential IT operations such as Google Kubernetes and Docker.
• Increases team building speed, resulting in faster design and delivery.

Conclusion

And here is why several business owners love the containers. Containers' benefits greatly exceed any downsides. Its simplicity of use, resource efficiency, simplicity, and universality makes it a strong frontrunner among coders.

The vast majority of service providers now implement cloud services, and it has shown to be a success, with increased speed capacity installations, easier expandability and versatility, and much fewer hours invested on multiple hardware data center equipment. Conventional cloud technology, on the opposite side, isn't suitable in every situation. Azure by Microsoft, Google Cloud Platform (GCP), and AWS by Amazon are all conventional cloud providers with data centers all over the globe. Although each supplier's data center capacity is continually growing, such cloud services providers are not near enough to clients whenever a program requires the best performance and low delay. Consider how aggravating it is to enjoy a multiplayer game and have the frame rate decrease, or to stream a video and have the visual or sound connection delay. Edge computing is useful whenever speed is important or produced data has to be kept near to the consumers (Shi et al., 2016). This article evaluates two approaches to edge computing: 'Edge virtual machines (VMs)' and 'Edge containers', and helps developers determine which would be ideal for business.

What is Edge Computing?#

There are just a few data center areas available from the main cloud service providers. Despite their remarkable computing processing capability, the three top cloud service providers have only roughly 150 areas, most of which are in a similar region. These only cover a limited portion of the globe. Edge computing is powered by a considerably higher number of tiny data centers all over the globe. It employs a point of presence (PoP), which is often placed near wherever data is accessed or created. These PoPs operate on strong equipment and have rapid, dependable network access (Shi and Dustdar, 2016). It isn't an "either-or" situation when it comes to choosing between standard cloud and edge computing. Conventional cloud providers' data centers are supplemented or enhanced by edge computing.

[Edge computing] ought to be the primary supplier in several situations such as:

Streaming - Instead of downloading, customers are increasingly opting to stream anything. They anticipate streams to start right away, creating this a perfect application for edge computing.

Gaming - Ultra-low lag is beneficial to high scores in games and online gameplay.

Manufacturing - In manufacturing, the Internet of Things (IoT) and operational technology (OT) offer exciting new ways to improve monitoring systems and administration as well as run machines.

Edge Virtual Machines (Edge VMs)#

In a nutshell, virtual machines are virtual machines regardless of wherever they operate. Beginning with the hardware layer, termed as a bare-metal host server, virtual machines depend on a hypervisor such as VMware or Hyper-V to distribute computational resources across distinct virtual machine cases. Every virtual machine is a self-contained entity with its OS, capable of handling almost any program burden. The flexibility, adaptability, and optimum durability of these operations are significantly improved by virtual machine designs. Patching, upgrades, and improvement of the virtual machine's OS are required on a routine basis. Surveillance is essential for ensuring the virtual machine instances' and underpinning physical hardware infrastructure's stability (Zhao et al., 2017). Backup and data restoration activities must also be considered. All this amounts to a lot of time spent on inspection and management.

Virtual machines (VMs) are great for running several apps on the very same computer. This might be advantageous based on the demand. Assume users wish to run many domains using various Tomcat or .NET platforms. Users can operate them simultaneously without interfering with some other operations. Current apps may also be simply ported to the edge using VMs. If users utilize an on-premises VM or public cloud infrastructure, users could practically transfer the VM to an edge server using a lifting and shifting strategy, wherein users do not even affect the configuration of the app configuration or the OS.

Edge Containers#

A container is a virtualized, separated version of a component of a programme. Containers can enable flexibility and adaptability, although usually isn't for all containers inside an application framework, only for the one that needs expanding. It's simple to spin up multiple versions of a container image and bandwidth allocation among them after developers constructed one. Edge containers, like the containers developers have already seen, aren't fully virtualized PCs. Edge containers only have userspace, and they share the kernel with other containers on the same computer (Pires, Simão, and Veiga, 2021). It is often misinterpreted as meaning that physical machines provide less separation than virtual ones. Containers operating on the very same server, for instance, utilize the very same virtualization layer and also have recourse to a certain OS. Even though this seldom creates issues, it can be a stumbling barrier for services that run on the kernel for extensive accessibility to OS capabilities.

Difference Between VMs and Edge Containers#

Edge containers are appropriate whenever a developer's software supports a microservice-based design, which enables software systems to operate and scale individually. There is also a reduction in administrative and technical costs. Since the application needs specific OS integration that is not accessible in a container, VM is preferred when developers need access to a full OS. VM is required for increased capabilities over the technology stack, or if needed to execute many programs on the very same host (Doan et al., 2019).

Conclusion#

Edge computing is a realistic alternative for applications that require high-quality and low-delay access. Conventional systems, such as those found in data centers and public clouds, are built on VMs and Edge containers, with little change. The only significant distinction would be that edge computing improves users' internet access by allowing them to access quicker (Satyanarayanan, 2017). Developers may pick what's suitable for their requirements now that they understand further about edge computing, such as the differences between edge VMs and edge containers.

Implementing AI: Artificial Intelligence at Edge is an interesting topic. We will dwell on it a bit more.

This is when things start to get interesting. However, a few extreme situations, such as Netflix, Spotify, and Amazon, are insufficient. Not only is it difficult to learn from extreme situations, but when AI becomes more widespread, we will be able to find best practices by looking at a wider range of enterprises. What are some of the most common issues? What are the most important and effective ways of dealing with them? And, in the end, what do AI-driven businesses look like?

Here are some of the insights gathered to capture, learn from, and share from approximately 2,500 white-collar decision-makers in the United States, the United Kingdom, Germany, India, and China who had all used AI in their respective firms. They were asked questions, and the responses were compiled into a study titled "Adopting AI in Organizations."

Speaking with AI pioneers and newcomers#

Surprisingly, by reaching out on a larger scale, a variety of businesses with varying levels of AI maturity were discovered. They were classified into three groups: AI leaders, AI-followers, and AI beginners, with the AI leaders having completely incorporated AI and advanced analytics in their organizations, as opposed to the AI beginners who are only starting on this road.

The road to becoming AI-powered is paved with potholes that might sabotage your development.

In sum, 99 percent of the decision-makers in this survey had encountered difficulties with AI implementation. And it appears that the longer you work at it, the more difficult it becomes. For example, 75 percent or more of individuals who launched their projects 4-5 years ago faced troubles. Even the AI leaders, who had more efforts than the other two groups and began 4-5 years ago, said that over 60% of their initiatives had encountered difficulties.

The key follow-up question is, "What types of challenges are you facing?" Do you believe it has something to do with technology? Perhaps you should brace yourself for a slight shock. The major issue was not one of technology. Rather, 91 percent of respondents stated they had faced difficulties in each of the three categories examined: technology, organization, and people and culture. Out of these categories, it becomes evident that people and culture were the most problematic. When it comes to AI and advanced analytics, it appears that many companies are having trouble getting their employees on board. Many respondents, for example, stated that staff was resistant to embracing new ways of working or that they were afraid of losing their employment.

As a result, it should come as no surprise that the most important strategies for overcoming challenges are all related to people and culture. Overall, it is clear that the transition to AI is a cultural one!

A long-term investment in change for Artificial Intelligence at Edge#

But where does this adventure take us? We assume that most firms embarking on an organizational transformation foresee moving from one stable state to a new stable one after a period of controlled turbulence. When we look at how these AI-adopting companies envisage the future, however, this does not appear to be the case!

Conclusion for Artificial Intelligence at Edge:#

To get a sense of what it'll be like to be entirely AI-driven, researchers looked to the AI leaders, who have gone the furthest and may have a better idea of where they're going. This group has already integrated AI into their business or plans to do so by the year 2021. You'd think that after properly implementing and delivering AI inside the organization, they'd be satisfied with their work. They're still not finished. Quite the contrary, they aim to invest much more in AI over the next 18 months and on a far larger scale than previously. The other two groups had far smaller investment plans.

Multi-access edge computing (MEC) has evolved as a viable option to enable mobile platforms to cope with computational complexity and lag-sensitive programs, thanks to the fast growth of the Internet of Things (IoT) and 5G connectivity. Computing workstations, on the other hand, are often incorporated in stationary access points (APs) or base stations (BSs), which has some drawbacks. Thanks to drones' portability, adaptability, and maneuverability, a new approach to drone-enabled airborne computing has lately received much interest (Busacca, Galluccio, and Palazzo, 2020). Drones can be immediately dispatched to defined regions to address emergency and/or unanticipated needs when the computer servers included in APs/BSs are overwhelmed or inaccessible. Furthermore, relative to land computation, drone computing may considerably reduce work latency and communication power usage by making use of the line-of-sight qualities of air-ground linkages. Drone computing, for example, can be useful in disaster zones, emergencies, and conflicts when grounded equipment is scarce.

Drones as the Next-Generation Flying IoT#

Drones will use a new low-power design to power the applications while remaining aloft, allowing them to monitor users and make deliveries. Drones with human-like intelligence will soon be able to recognize and record sportsmen in action, follow offenders, and carry things directly to the home. But, like with any efficient system, machine learning may consume energy, thus research on how to transfer a drone's computing workloads to a detector design to keep battery use low to keep drones flying for very much longer is necessary. Drones are a new type of IoT gadget that flies through the air with complete network communication capabilities (Yazid et al., 2021). Smart drones with deep learning skills must be able to detect and follow things automatically to relieve users of the arduous chore of controlling them, all while operating inside the power constraints of Li-Po batteries.

Drone-assisted Edge Computing#

The 5G will result in a significant shift in communications technologies. 5G will be required to handle a huge amount of customers and networking equipment with a wide range of applications and efficiency needs (Hayat et al., 2021). A wide range of use instances will be implemented and back, with the Internet of Things (IoT) becoming one of the most important due to its requirement to communicate a large number of devices that collect and transmit information in numerous different applications such as smart buildings, smart manufacturing, and smart farming, and so on. Drones could be used to generate drone cells, which also discusses the requirement for combining increasing pressure of IoT with appropriate consumption of network resources, or perhaps to establish drones to deliver data transmission and computer processing skills to mobile users, in the incident of high and unusual provisional incidents generating difficult and diverse data-traffic volume.

How AI at the Edge Benefits Drone-Based Solutions#

AI is making inroads into smart gadgets. The edge AI equipment industry is growing at a quicker rate due to the flexibility of content operations at the edge. Data accumulation is possible with edge technology. Drones, retail, and business drones are rising in popularity as edge equipment that creates data that has to be processed. Drones with Edge AI are better for construction or manufacturing, transportation surveillance, and mapping (Messous et al., 2020). Drones are a form of edge technology that may be used for a variety of tasks. Visual scanning, picture identification, object identification, and tracking are all used in their work. Drones using artificial intelligence (AI) can recognize objects, things, and people in the same manner that humans can. Edge AI enables effective analysis of the data and output production based on data acquired and delivered to the edge network by drones, and aids in the achievement of the following goals:

• Object monitoring and identification in real-time. For security and safety purposes, drones can monitor cars and vehicular traffic.
• Infrastructure that is aging requires proactive upkeep. Bridges, roads, and buildings degrade with time, putting millions of people in danger.
• Drone-assisted surveillance can help guarantee that necessary repairs are completed on time.
• Face recognition is a technique for recognizing someone's face whereas this prospect sparks arguments about the technology's morality and validity, AI drones with face recognition can be beneficial in many situations.

Drones may be used by marketing teams to track brand visibility or gather data to evaluate the true influence of brand symbol installation.

Challenges in Drone-Assisted Edge Computing#

Drone computing has its own set of challenges such as:

• Drone computing differs greatly from ground computation due to the extreme movement of drones. Wireless connectivity to/from a drone, in particular, changes dramatically over time, necessitating meticulous planning of the drone's path, task distribution, and strategic planning.
• Computational resources must also be properly apportioned over time to guarantee lower data energy usage and operation latency. A drone's power flight plan is critical for extending its service duration (Sedjelmaci et al., 2019).
• Due to a single drone's limited computing capability, many drones should be considered to deliver computing services continuously, where movement management, collaboration, and distribution of resources of numerous drones all necessitate sophisticated design.

Conclusion#

In drone computing, edge technology guarantees that all necessary work is completed in real-time, directly on the spot. In relief and recovery efforts, a drone equipped with edge technology can save valuable hours (Busacca, Galluccio, and Palazzo, 2020). Edge computing, and subsequently edge AI, have made it possible to take a new and more efficient approach to information analysis, resulting in a plethora of information drone computing options. Drones can give value in a range of applications that have societal implications thanks to edge technology. [Edge data centres] will likely play a key part in this, maybe aiding with the micro-location data needed to run unmanned drone swarms in the future. Increasing commercial drone technology does have the ability to provide advantages outside of addressing corporate objectives.

Read more about the Other Edge Computing Usecases.

Mobile Edge Computing (MEC) can aid is with real estate property browsing. MEC can provide a two-fold answer. Most buyers look at many residences and don't make decisions without viewing them. Engaging Mobile Edge Computing (MEC) applications like augmented reality (AR) and virtual reality (VR) demonstrate strong opportunities to connect the external and simulated worlds, whether it's putting a virtual couch in your sitting room as part of an interactive retail setting or allowing forecasting refurbishment steered by actual data as well as a layering of step-by-step graphic guidelines. As an element of synchronized and safe processes, the objective is to allow all sides to see what other sees. Combining smartphones and tablets, iPads, and smartwatches with virtual collaborative technologies redefine learning and allow product specialists to help from a distance (Ambrose and Shen, 2021). The goal is to make the distant assessment, replacement, and service of existing goods more efficient.

AR (augmented reality) and VR (virtual reality) are still considered specialized innovations which have yet to be widely accepted. A lot of it comes down to the issues that edge computing can now solve. Following the commercial release of 5G, AR (augmented reality) and VR (virtual reality) encompasses a slew of innovative application cases that, when combined by the edge of the network, will provide significant value to the sector and businesses. Applying virtual layers to live sights is what augmented reality is all about. It can be performed with a device, but in business, wearable technology is much more probably to be used. VR is total absorption in a digital perspective that requires the use of a set of glasses that block the user's view of the world surrounding them (Gerasimova, 2019).

The real estate sector is likely to be transformed by this technology, which some belief would make property hunting more effective. It can help purchaser's picture houses in progress and alleviate the stress of moving to the new location or purchasing from overseas.

What role does Edge and AR play in wooing customers in property hunting?#

With AR (augmented reality), real estate reaches new heights in terms of providing consumers with a more efficient and interesting visual journey. Retailers may now transport them to any destination they like. Offer visitors a digital tour to relieve the stress of having to figure out road signs and building numbers while travelling. People will also have a complete image of the place after they have had the opportunities to explore it. Aside from the ease, it provides to property buyers, it also assists real estate brokers in other ways. Augmented Reality may also be used for branding and advertising (Lang and Sittler, 2012).

The following are some of the marketing aspects of augmented reality for property investment:

• More dynamic print catalogues and large boards are being developed.
• Spatial that really can help for-sale properties in real-time.
• Get an interactive function in the app so a potential buyer may reach out to the retailer right away.
• A larger audience

How to Use Virtual Reality in Property Hunting?#

Virtual reality performs a vital part in the property market, from real estate development to housing developments. Let's take a look at several ways that may use virtual reality property hunting:

• Guided Visits: Property hunters, on the whole, compile a list of properties they wish to see and then go to the locations. Some residences are nearby, while some are on the periphery. As a result, planning a visit and narrowing down a list of prospective homes becomes physiologically and psychologically demanding. VR in the housing market efficiently overcomes all of these issues (Pleyers and Poncin, 2020).

• Participatory Visits: Participatory visits are growing in popularity these days. The key difference between supervised and participatory trips would be that active trips allow property hunters to tap on the display and zoom in on certain areas of the property.

• Virtual Staging: The term "virtual staging" refers to the technique of electronically furnishing vacant places. Simply defined, VS is a property investment internet marketing tactic that lets customers see themselves in completely furnished homes.

• Communication: Modern residences and ultra-luxury homes now provide a variety of public utilities. While such products and services provide convenience, they may also be perplexing sometimes.

The Benefits of Edge-VR in Property Hunting:#

• Time and money-saving.
• Creates an emotional bond.
• Profits increased.
• Experimentation is simple.
• Reach Out to a Larger Audience

What is the difference between VR and AR?#

AR and VR are both disruptive technologies, they have some significant distinctions:

Conclusion for Edge and AR#

Several businesses that are willing to embrace augmented reality are unable to do so due to limitations in their capacity to exchange data on the cloud. Companies may utilise graphical tools and applications like Zoom or Microsoft Exchange for normal communication, but they can't use the same cloud-based solutions for critical organisational activities like learning, support, or technical access because of data security and privacy ownership issues. AR and VR are on the verge of allowing participants to take their immersive experience with others, which is something that most people like about property hunting. In terms of what's feasible, both AR and VR are advancing at breakneck speed (Deaky and Parv, 2017). It's nearly a perfect match for property hunting.

To know the benefits of Edge Computing please read: Differentiation Between Edge Computing and Cloud Computing

What are Smart Stadiums? Can intelligent Edge be used for Smart Stadiums and Sports in general? Find out below.

Smart Stadiums#

Fans expect high-definition, real-time streaming on their devices and computers at today's sports activities. Games can be held in an arena, in various locations, or outside. Especially outside competitions range from fixed-track contests to competitions that begin in one area and conclude hundreds of kilometers and perhaps even days back. Stations employ High-Definition (HD) equipment to live to transmit programming in these places. Huge volumes of visual data are generated by these devices. This information must be handled and examined. The worldwide video streaming business is expected to hit \$240 billion by 2030, according to estimates (Kariyawasam and Tsai, 2017). It's difficult to imagine a market wherein live broadcast streaming isn't an essential component, thanks to the entertainment and media businesses, which have been supported by an ever-increasing amount of lateral use scenarios.

Sports Live stream with Smart Edge-computing Frameworks for Stadiums#

Edge computing, sometimes known as smart edge computer technology or just "edge," maintains graphics processing locally, low latency, and traffic while also removing the need for costly transport cables. Edge designs save substantial amounts of network transport traffic by drastically lowering video delay. As a result, onsite visitors will have a good user experience and procedures will be more effective. Many types of application scenarios are supported by the edge, including visual information sharing between both the edge and multiple clouds either between edge nodes (Bilal and Erbad, 2017). Edge allows streamers to send enhanced and processed footage to the server for extended storage. Edge technology for real-time video augments cloud capability by doing numerous visual processing activities onsite, complementing cloud capabilities.

Edge-Based Deployment#

Video data is transferred to a cloud data centre in a cloud-only architecture. This might result in increased delay, making it even harder for transmitters to provide pleasant television quality to paying customers. Conventional cloud-based options need a substantial expenditure in backhaul hardware, fibre lines, and satellite connectivity, among other things. Edge computing implements a decentralized and multi-layered framework for successfully constructing live video systems. Edge nodes may combine all of the capabilities of a centralized server regionally, resulting in increased organizational effectiveness. Additional capabilities, which include image processing and information security, may be hosted on the very same architecture with no need to create a distinct connection to maintain (Wang and Binstin, 2020). Compatibility is a basic architectural principle of edge networks, making it much easier to introduce additional applications to the very same system. The edge platform's multi-tenancy feature allows multiple parties' contract to execute their respective applications on the very same network edge.

Edge-Delivered streaming sequence#

The procedure for producing live stream broadcasts uses an edge that includes:

• Technology for streaming video is rapidly advancing, and HD equipment is now in use at every sports event all over the globe.
• To gather and combine information from numerous cameras, local edge-based multimedia processors could be placed all along the path.

• Whenever a smartphone or tablet asks for video streaming or live stream, the edge node establishes a communication link with the end devices.

• People who are at sporting events may keep an eye on the competitors and then use their smartphones and tablets to view live video streaming of the sport from beginning to end.

• Huge volumes of data are generated by the Camera system. This information must be transported to the cloud for graphics processing under a cloud services approach. As a result, backhaul capacity is quite costly. Traffic will impair the quality of the video if capacity is inadequate. It may also have an impact on other programs that use the backhaul network (Dautov and Distefano, 2020).

Intelligent Edge at Sports Streaming Enables the Following Features#

Connectivity, communications, and interfacing requirements are all provided by the smart edge computing method, allowing for real-time, streaming video during sporting events.

• Security: With computation to networking transfer, the intelligent edge safeguards visual data at all logical layers.
• Scalability: Edge can shift memory and computing capabilities among inactive and active nodes for scalability.
• Open: Various carriers' edge node architecture and streaming platforms from different suppliers will collaborate.
• Autonomy: Edge-based live stream solutions are self-contained and may function without the use of the cloud (Abeysiriwardhana, Wijekoon and Nishi, 2020).
• Reliability: In higher edge nodes, framework administration can be set and provide management solutions.
• Agility: Without using cloud services, live stream video is analyzed and transmitted between edge nodes.

Streaming Contracts#

The licenses to live-streamed sporting events are controlled by numerous teams and leagues, who license such assets to different Television stations and, progressively, streaming sites. However, in addition to financial price and conditions of the contracts, broadcast rights transactions must typically specify the breadth of the materials being licensed, yet if the license is exclusionary, the relevant area, and, in many cases, the rights holder's advertising prospects (Secular, 2018). In the case of streaming services, each has its system of defined issues to address.

Exclusivity and Range of Streaming Contracts#

There have rarely existed greater options for sports to engage viewers, whether, through broadcasting, television, or online means of displaying programming, and they are motivated to use them all. Stations that have their streaming platforms are attempting to widen the range of licenses as often as feasible to protect any remaining television income while attracting new digital customers. Streaming services have the chance to accelerate the change in how people follow by having sports entirely available online.

Conclusion for Smart Stadiums#

[Edge technology] for streaming sports video enhances cloud capacity by doing a variety of visual data processing on-site. As streaming companies continue to demonstrate that sports can be viewed completely online, more industry heavyweights may decide to enter the fray (Mathews, 2018). The corporation hoping to have control over sports streaming rights should carefully assess the breadth of the rights they are licensing, balancing financial concerns with exclusivity. Lastly, as streaming platforms innovate and change how people watch sports, they should ensure that their Terms and Conditions are thorough and compatible with the terms & conditions of streaming contracts.

Introduction#

Sports fans are seeking an enhanced experience through their portable devices in this era of online and mobile usage. As consumers grow more intelligent and demand interactive, inventive, and entertaining experiences, the number of virtual events is expanding. This pushes the envelope for the style and durability of events. The future development of cellular wireless communication technology can produce improved engagement, changing how audiences experience sports, including live-streaming video, 3D virtual interactions, and real-time access to sports statistics. The integration of 5G, AR, and VR in sports allows for entirely new user interactions, breaking limits and bringing the audience closer to the action. In an evolving sports network, connectivity and flexibility offer new benefits for teams playing in front of crowded arenas or single racers on a wooded course. This is why 5G can become a valuable resource for the sports industry as it strives to revolutionize audience engagement both at home and in the stadium. Sporting activities might offer a greater experience for both the traveling fan who attends each event live and the die-hard fan who watches every event on TV.

5G is a Dependable and Tremendously Fast Network#

5G is 5 to 20 times more efficient than 4G. It can broadcast and read packets almost instantly, with times as low as 10 milliseconds in certain conditions. Beyond high-speed internet connections, there will be significant improvements in the reliability and performance of visual and voice calls, as well as faster playback. Due to its speed and latency, 5G will facilitate technological advances such as AR and VR, touch-capable devices, robotics, self-driving vehicles, and the IoT. Furthermore, it can be used in conjunction with Artificial Intelligence and machine learning. 5G is a game-changer, with the potential to usher in the next technological revolutions.

Influence of 5G in Sports (Present and Future)#

The increased capacity and reduced latency of 5G will unlock a variety of new capabilities for spectators and athletes alike. Here are some advantages:

A Thrilling and Comprehensive Stadium Experience#

Sports fans are searching for new ways to interact with the game on a virtual level. With the emergence of 360º camera systems, AR, and VR, there is an opportunity to develop more realistic fan interactions. Fans may stroll the sidelines, see from the athletes' perspectives, and enjoy celebrations in the dressing room, all from the comfort of their homes. 5G could add a new level of sophistication to stadium experiences. Real-time AR technologies and immersive VR options will enhance pre-game festivities and allow spectators to experience 4K/UHD data without a large physical display. Fans could also explore various parts of the event virtually as if they were there in person.

Creating an Integrated Arena#

Attending live sports events requires a positive stadium environment. 5G can enhance this experience by connecting equipment in real-time with incredibly low latency, creating new possibilities. It could improve the overall environment for spectators by providing high-quality video streaming and new perspectives from 360º, ultra-high-resolution VR cameras using smartphones.

Digital Transformation of Sports#

The sports and entertainment sectors are leveraging 5G to transform fan experiences. Telecommunications operators, organizations, clubs, event coordinators, and media firms are all investing in this technology. Key focus areas for the digital transformation of sports include:

1. Improve the live experience for fans at venues.
2. Bring fans at home closer to the action.
3. Integrate pre and post-event activities into the holistic experience.
4. Develop experience-centric sports districts.

Conclusion for 5G in Sports#

The launch of 5G will significantly impact the sporting industry. It will not only provide lightning-fast speeds but also support advanced technologies like VR and AR, and enhance network connectivity. Fans, players, trainers, venues, and spectators will all benefit. 5G also enables fixed wireless connectivity for higher-quality streaming in 4K, 360 videos, or AR/VR formats in areas without fiber connectivity. The deployment of 5G in sports arenas will create a broad framework supporting various applications, allowing fans to experience performances in real-time during practice and competition. This presents a significant opportunity for network operators to deploy upgraded connections in sports stadiums and ensure effective engagement. 5G is poised to revolutionize sports with fresh applications, and the transformation is already underway.

Those who specialize in cyberspace and data security have been encouraging IT executives and internet providers to adapt to the challenges of a dynamic and fast-changing digital environment. With the operationalization of 5G networks, market expectations and the supply of new capabilities are rapidly increasing. For telecommunications companies, 5G represents a substantial opportunity to enhance consumer experiences and drive sales growth. Not only will 5G provide better internet connectivity, but it will also enable life-changing innovations that were once only imagined in sci-fi (Al-Dunainawi, Alhumaima, & Al-Raweshidy, 2018). While 5G connection speeds and accessibility have received much attention, understanding 5G's early prototype aspirations and its perception in network services is also crucial.

5G's Expectations Beyond Cloud Computing Companies#

The challenges of managing business development scenarios will be compounded by the complexities introduced by 5G. Some organizations may find themselves unprepared for these developments, facing challenges such as poor bandwidth and performance, especially if operating at frequencies below 6 gigahertz. However, true 5G promises capabilities that extend from utility and industrial grids to autonomous vehicles and retail applications, potentially transforming network edges (Jabagi, Park, & Kietzmann, 2020). For those unprepared, the ability to handle data could degrade significantly, leading to major latency issues and a compromised experience for both consumers and staff.

5G's Expectations Are Only the Beginning of the Challenge#

Implementing adequate protection to safeguard customers and crucial data could lead to congestion within systems. Ensuring that applications operate effectively at 5G speeds is one challenge; guaranteeing safety over an expanding network poses additional issues (Lee, 2019). Cloud computing companies face limitations in addressing these challenges.

It Will Be Necessary to Plan Carefully#

Cybersecurity professionals are considering two main approaches to address 5G issues: handling security procedures of the 5G base on the operator side or addressing edge protection where 5G acts as a fallback or gateway node, often as part of an SD-WAN implementation. Both strategies will require automation and artificial intelligence capabilities to keep up with conventional edge demands. Additional high-performance protection at the cloud edge will also be necessary (Ahamed & Faruque, 2021). Integrated systems must scale up with additional virtual machines and filters while scaling down by adding new elements to manage increased demand and ensure smooth, effective, and safe operations. As 5G accelerates commerce and applications, it will also speed up cyber-attacks.

Addressing 5G's Expectation Problems Is Not a Choice#

Currently, 5G generates around $5 billion in annual revenue for operators, expected to rise to $357 billion by 2025. This shift necessitates significant adjustments in the deployment and usage of 5G. Many businesses lack the expertise to meet these requirements. The pursuit of the best products and systems has led to complex, hard-to-implement systems. Under 5G's pressure, these systems may perform poorly (Guevara & Auat Cheein, 2020). Historically, cybersecurity aimed to balance safety with connectivity and efficiency. As internet providers and security groups face mounting challenges, the shift to 5G represents only the beginning of a current paradigm shift.

Five Approaches to Improve the 5G User Experience#

1. Close the knowledge gap to effectively teach and advertise the benefits of 5G.
2. Ensure high consistency in both indoor and outdoor services.
3. Accelerate the commercialization of new and existing application cases.
4. Address the network infrastructure demands driven by new internet services (Lee, 2019).
5. Consider customer desires to envision new applications.

Conclusion#

5G is driving the development of innovative application cases and commercial opportunities, such as mobile gaming, fixed wireless access, and enhanced consumer experiences. As 5G expands, it will dramatically impact data retrieval, causing significant latency issues and affecting the user experience (Ahn, 2021). The window of opportunity for solutions to meet 5G demands is closing. Companies must act swiftly to capitalize on this opportunity and prepare for the evolving demands of 5G and the imminent arrival of 6G.

More 5G-based cloud computing companies will emerge to meet the needs of the 5G environment.

Edge technology as a modern industry sprung up as a result of the shift of processing from cloud to edge. Cloud gaming services is booming as a result of the need of more low latency benefits for the end users.

As the gadgets interconnected to the internet grows and their possibilities improve, so too does the demand for real making decisions free of cloud computing's delay and, sometimes in circumstances, connection. Edge technology is a modern industry that has sprung up as a result of the shift of processing resources from the cloud to the edge. Edge computing gives proper local machine learning to gadgets without the need to contact the cloud to make conclusions. IoT gadgets function under settings that vary from some of those found in corporate offices, necessitating the establishment of a new range of components to enable processing in such locations. The expanding usage of cloud-based AI techniques like machine learning techniques is pushing developments in hardware designs that can keep up with the applications' voracious need for computing power and storage capacity (Gan et al., 2019). Without developments in technology, technologies such as instant-booting PCs, cell phones, jaw-dropping video game graphics, lightning-fast in-memory analytics, and hugely spacious memory devices would be significantly more restricted or prohibitively costly.

Edge Computing#

Edge computing is a decentralized IT framework inside which customer data is analysed as near to the original point as feasible at the platform's perimeter. Edge computing relocates certain memory and computation capabilities away from the main data centre and nearer to the raw data. Instead of sending unprocessed information to a data centre for analysis and interpretation, this process is carried in which the information is captured, whether in a retail outlet, a manufacturing floor, a large utility, or throughout a smart city Coppolino et al., 2019. IT and corporate computing are being reshaped by edge computing.

Edge computing Hardware?#

The structural characteristics and capabilities required to operate a program at the edge are referred to as edge computing hardware. Centres, CPUs, networking devices, and endpoint devices are among these technologies (Capra et al.,2019) . Edge Ecosystem Analyzer is used to learn about additional aspects of the edge value chain.

Impact of Edge Computing on Hardware for Cloud Gaming#

Edge computing has a wide range of functions that work in a variety of circumstances and environments. Dependent on various application scenarios and sectors, they have various hardware needs. It's no coincidence that several businesses are moving to the edge as connection improves and the development of low-delay "real-time" data processing grows. With this change, nevertheless, there seems to be a significant necessity for edge computing gear to be created for unique circumstances for its many business applications, each with its own set of hardware specifications (Satyanarayanan et al., 2021) . For instance, in automated vehicles, ultimate decision-making is required for movement control, therefore increased hardware is a requirement owing to the massive volumes of data being analysed in real-time; but, thanks to the car's limited space, equipment design is indeed a limitation.

Gaming on the Edge ( and Cloud)#

The majority of game computation is now performed on gadgets directly. Although some computing may be performed on a remote server — where a gadget can transmit information to be analyzed and then delivered into these systems is often located far away in enormous data centres, which implies the time it would take for data to be delivered will eventually diminish the gaming performance. Rather than a single huge remote server, mobile edge computing depends on multiple small distribution centres that are located in a nearer close presence (Braun et al., 2017). So because gadgets won't just have to transfer information to a data computer, analyze it, and afterwards deliver the data, MEC can preserve computing power on gadgets for a smoother, quicker gameplay experience.

Cloud computing#

Something that includes offering distributed services via the internet is referred to as cloud computing. IaaS, PaaS, and SaaS are the three basic forms of cloud computing technology. It is possible to have a business or government cloud. Everyone on the internet may buy services from a cloud platform (Younas et al., 2018). A private cloud is a closed network or data centre that provides a platform as a service to a small group of individuals with policy actions and privileges. The purpose of cloud computing, whether business or government, is to give quick, flexible access to network infrastructure and IT applications.

Cloud infrastructure and hardware#

Cloud infrastructure is a word that refers to the hardware, abstract services, memory, and networking capacity that are required for cloud computing. Consider cloud infrastructure to be the technologies required to create a cloud. Cloud infrastructure is required to operate operations and services in the cloud.

Cloud Gaming#

Cloud gaming refers to the practice of playing games on servers located remotely in cloud services. On either a PC or smartphone, so no need to acquire and download games. Rather, to deliver game data to an application or website loaded on the target device, streaming sites create a steady internet service. The action is generated and performed on a distant server, yet everything here is seen and interacted with directly on the devices. Throughout most situations, cloud computing gaming involves an annual or monthly membership to obtain the game. Some applications need the acquisition of games in addition to the charge (Choy et al., 2014). Customized or internet apps are frequently given by cloud gaming solutions to stream sports.

Conclusion#

The role of the network is changing when it comes to offering exceptional experiences with these new interactions. The growing use of cloud-based AI techniques such as machine learning is driving hardware innovations that can keep up with the applications' insatiable need for computational power and storage space. Edge computing encompasses a wide range of capabilities that may be used in some situations and contexts (Gan et al., 2019). Cloud gaming is booming, due in part to the global coronavirus outbreak and broad implementation of shelter-in-place rules. Gaming is a tremendous technical platform that can be applied to a wide range of sectors, including Edge, Cloud and Hardware.

Read More about Edge Gaming

Can an AI-based edge computing platform drive businesses or is that a myth? We explore this topic here._

Introduction#

For a long time, artificial intelligence has been a hot issue. We've all heard successful tales of forward-thinking corporations creating one brilliant technique or another to use Artificial Intelligence technology or organizations that promise to put AI-first or be truly "AI-driven." For a few years now, Artificial Intelligence (AI) has been impacting sectors all around the world. Businesses that surpass their rivals are certainly employing AI to assist in guiding their marketing decisions, even if it isn't always visible to the human eye (Davenport et al., 2019). Machine learning methods enable AI to be characterized as machines or processes with human-like intelligence. One of the most appealing features of AI is that it may be used in any sector. By evaluating and exploiting excellent data, AI can solve problems and boost business efficiency regardless of the size of a company (Eitel-Porter, 2020). Companies are no longer demanding to be at the forefront or even second in their sectors; instead, businesses are approaching this transition as if it were a natural progression.

Artificial Intelligence's (AI-driven) Business Benefits#

Businesses had to depend on analytics researchers in the past to evaluate their data and spot patterns. It was practically difficult for them to notice each pattern or useful bit of data due to the huge volume of data accessible and the brief period in their shift. Data may now be evaluated and processed in real-time thanks to artificial intelligence. As a result, businesses can speed up the optimization process when it comes to business decisions, resulting in better results in less time. These effects can range from little improvements in internal corporate procedures to major improvements in traffic efficiency in large cities (Abduljabbar et al., 2019). The list of AI's additional advantages is nearly endless. Let's have a look at how businesses can benefit:

• A More Positive Customer Experience: Among the most significant advantages of AI is the improved customer experience it provides. Artificial intelligence helps businesses to improve their current products by analyzing customer behavior systematically and continuously. AI can also help engage customers by providing more appropriate advertisements and product suggestions (Palaiogeorgou et al., 2021).

• Boost Your Company's Efficiency: The capacity to automate corporate procedures is another advantage of artificial intelligence. Instead of wasting labor hours by having a person execute repeated activities, you may utilize an AI-based solution to complete those duties instantly. Furthermore, by utilizing machine learning technologies, the program can instantly suggest enhancements for both on-premise and cloud-based business processes (Daugherty, 2018). This leads to time and financial savings due to increased productivity and, in many cases, more accurate work.

• Boost Data Security: The fraud and threat security capabilities that AI can provide to businesses are a major bonus. AI displays usage patterns that can help to recognize cyber security risks, both externally and internally. An AI-based security solution could analyze when specific employees log into a cloud solution, which device they used, and from where they accessed cloud data regularly.

Speaking with AI Pioneers and Newcomers#

Surprisingly, by reaching out on a larger scale, researchers were able to identify a variety of firms at various stages of AI maturity. Researchers split everyone into three groups: AI leaders, AI followers, and AI beginners (Brock and von Wangenheim, 2019). The AI leaders have completely adopted AI and data analysis tools in their company, whilst the AI beginners are just getting started. The road to becoming AI-powered is paved with obstacles that might impede any development. In sum, 99% of the survey respondents have encountered difficulties with AI implementation. And it appears that the more we work at it, the more difficult it becomes. 75% or more of individuals who launched their projects 4-5 years ago faced troubles. Even the AI leaders, who had more effort than the other two groups and began 4-5 years earlier, had over 60% of their projects encounter difficulties. When it comes to AI and advanced analytics, it appears that many companies are having trouble getting their employees on board. The staff was resistant to embracing new methods of working or were afraid of losing their employment. Considering this, it should be unsurprising that the most important tactics for overcoming obstacles include culture and traditions (Campbell et al., 2019). Overall, it's evident that the transition to AI-driven operations is a cultural one!

The Long-Term Strategic Incentive to Invest#

Most firms that begin on an organizational improvement foresee moving from one stable condition to a new stable one after a period of controlled turbulence (ideally). When developers look at how these AI-adopting companies envision the future, however, this does not appear to be the case. Developers should concentrate their efforts on the AI leaders to better grasp what it will be like to be entirely AI-driven since these are the individuals who've already progressed the most and may have a better understanding of where they're headed. It's reasonable to anticipate AI leaders to continue to outpace rival firms in the future (Daugherty, 2018). Maybe it's because they have a different perspective on the current, solid reality that is forming. The vision that AI leaders envisage is not one of consistency and "doneness" in terms of process. Consider a forthcoming business wherein new programs are always being developed, with the ability to increase efficiency, modify job processing tasks, impact judgment, and offer novel issue resolution. It appears that the steady state developers are looking for will be one of constant evolution. An organization in which AI implementation will never be finished. And it is for this reason that we must start preparing for AI Edge Computing Platform to pave the way for the future.

References#

• Abduljabbar, R., Dia, H., Liyanage, S., & Bagloee, S.A. (2019). Applications of Artificial Intelligence in Transport: An Overview. Sustainability, 11(1), p.189. Available at: link.
• Brock, J.K.-U., & von Wangenheim, F. (2019). Demystifying AI: What Digital Transformation Leaders Can Teach You about Realistic Artificial Intelligence. California Management Review, 61(4), pp.110–134.
• Campbell, C., Sands, S., Ferraro, C., Tsao, H.-Y. (Jody), & Mavrommatis, A. (2019). From Data to Action: How Marketers Can Leverage AI. Business Horizons.
• Daugherty, P.R. (2018). Human + Machine: Reimagining Work in the Age of AI. Harvard Business Review Press.
• Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2019). How Artificial Intelligence Will Change the Future of Marketing. Journal of the Academy of Marketing Science, 48(1), pp.24–42. Available at: link.
• Eitel-Porter, R. (2020). Beyond the Promise: Implementing Ethical AI. AI and Ethics.
• Palaiogeorgou, P., Gizelis, C.A., Misargopoulos, A., Nikolopoulos-Gkamatsis, F., Kefalogiannis, M., & Christonasis, A.M. (2021). AI: Opportunities and Challenges - The Optimal Exploitation of (Telecom) Corporate Data. Responsible AI and Analytics for an Ethical and Inclusive Digitized Society, pp.47–59.

There is a common debate on how Edge Computing Platforms for Anomalies Detection can be used. In this blog, we will cover details about it.

Introduction#

Anomalies are a widespread problem across many businesses, and the telecommunications sector is no exception. Anomalies in telecommunications can be linked to system effectiveness, unauthorized access, or forgery, and therefore can present in a number of telecommunications procedures. In recent years, artificial intelligence (AI) has become more prominent in overcoming these issues. Telecommunication invoices are among the most complicated invoices that may be created in any sector. With such a large quantity and diversity of goods and services available, mistakes are unavoidable. Products are made up of product specifications, and the massive amount of these features, as well as their numerous pairings, gives rise to such diversity (Tang et al., 2020). Goods and services – and, as a result, the invoicing process – are becoming even more difficult under 5G. Various corporate strategies, such as ultra-reliable low-latency communication (URLLC), enhanced mobile broadband (eMBB), and large machine-type communication, are being addressed by service providers. Alongside 5G, the 3GPP proposed the idea of network slicing (NW slice) and the related service-level agreements (SLAs), adding still another layer to the invoicing procedure's complexities.

How Do Network Operators Discover Invoice Irregularities?#

Invoice mistakes are a well-known issue in the telecom business, contributing to invoicing conflicts and customer turnover. These mistakes have a significant monetary and personal impact on service providers. To discover invoice abnormalities, most network operators use a combination of traditional and computerized techniques. The manual method is typically dependent on sampling procedures that are determined by company regulations, availability of materials, personal qualities, and knowledge. It's sluggish and doesn't cover all of the bills that have been created. These evaluations can now use regulation digitization to identify patterns and provide additional insight into massive data sets, thanks to the implementation of IT in business operations (Preuveneers et al., 2018). The constant character of the telecom business must also be considered, and keeping up would imply a slowdown in the introduction of new goods and services to the marketplace.

How AI and Machine Learning Can Help Overcome Invoice Anomaly Detection#

An AI-based system may detect invoicing abnormalities more precisely and eliminate false-positive results. Non-compliance actions with concealed characteristics that are hard for humans to detect are also easier to identify using AI (Oprea and Bâra, 2021). Using the procedures below, an AI system learns to recognize invoice anomalous behavior from a collection of data:

1. Data from invoices is incorporated into an AI system.
2. Data points are used to create AI models.
3. Every instance a data point detracts from the model, a possible invoicing anomaly is reported.
4. The invoice anomaly is approved by a specific domain.
5. The system applies what it has learned from the activity to the data model for future projections.
6. Patterns continue to be collected throughout the system.

Before delving into the details of AI, it's vital to set certain ground rules for what constitutes an anomaly. Anomalies are classified as follows:

• Point anomalies: A single incident of data is abnormal if it differs significantly from the others, such as an unusually low or very high invoice value.
• Contextual anomalies: A data point that is ordinarily regular but becomes an anomaly when placed in a specific context.
• Collective anomalies: A group of connected data examples that are anomalous when viewed as a whole but not as individual values. When many point anomalies are connected together, they might create collective anomalies (Anton et al., 2018).

Implications of AI and Machine Learning in Anomaly Detection#

All sectors have witnessed a significant focus on AI and Machine Learning technologies in recent years, and there's a reason why AI and Machine Learning rely on data-driven programming to unearth value hidden in data. AI and Machine Learning can now uncover previously undiscovered information and are the key motivation for their use in invoice anomaly detection (Larriva-Novo et al., 2020). They assist network operators in deciphering the unexplained causes of invoice irregularities, provide genuine analysis, increased precision, and a broader range of surveillance.

Challenges of Artificial Intelligence (AI)#

The data input into an AI/ML algorithm is only as strong as the algorithm itself. When implementing the invoice anomaly algorithm, it must react to changing telecommunications data. Actual data may alter its features or suffer massive reforms, requiring the algorithm to adjust to these changes. This necessitates continual and rigorous monitoring of the model. Common challenges include a loss of confidence and data skew. Unawareness breeds distrust, and clarity and interpretability of predicted results are beneficial, especially in the event of billing discrepancies (Imran, Jamil, and Kim, 2021).

Conclusion for Anomaly Detection#

Telecom bills are among the most complicated payments due to the complexity of telecommunications agreements, goods, and billing procedures. As a result, billing inconsistencies and mistakes are widespread. The existing technique of manually verifying invoices or using dynamic regulation software to detect anomalies has limits, such as a limited number of invoices covered or the inability to identify undefined problems. AI and Machine Learning can assist by encompassing all invoice information and discovering different anomalies over time (Podgorelec, Turkanović, and Karakatič, 2019). Besides invoice anomalies, a growing number of service providers are leveraging AI and Machine Learning technology for various applications.

References#

• Anton, S.D., Kanoor, S., Fraunholz, D., & Schotten, H.D. (2018). Evaluation of Machine Learning-based Anomaly Detection Algorithms on an Industrial Modbus/TCP Data Set. Proceedings of the 13th International Conference on Availability, Reliability and Security.
• Imran, J., Jamil, F., & Kim, D. (2021). An Ensemble of Prediction and Learning Mechanism for Improving Accuracy of Anomaly Detection in Network Intrusion Environments. Sustainability, 13(18), p.10057.
• Larriva-Novo, X., Vega-Barbas, M., Villagrá, V.A., Rivera, D., Álvarez-Campana, M., & Berrocal, J. (2020). Efficient Distributed Preprocessing Model for Machine Learning-Based Anomaly Detection over Large-Scale Cybersecurity Datasets. Applied Sciences, 10(10), p.3430.
• Oprea, S.-V., & Bâra, A. (2021). Machine learning classification algorithms and anomaly detection in conventional meters and Tunisian electricity consumption large datasets. Computers & Electrical Engineering, 94, p.107329.
• Podgorelec, B., Turkanović, M., & Karakatič, S. (2019). A Machine Learning-Based Method for Automated Blockchain Transaction Signing Including Personalized Anomaly Detection. Sensors, 20(1), p.147.
• Preuveneers, D., Rimmer, V., Tsingenopoulos, I., Spooren, J., Joosen, W., & Ilie-Zudor, E. (2018). Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study. Applied Sciences, 8(12), p.2663.
• Tang, P., Qiu, W., Huang, Z., Chen, S., Yan, M., Lian, H., & Li, Z. (2020). Anomaly detection in electronic invoice systems based on machine learning. Information Sciences, 535, pp.172–186.

Introduction#

5G has been substantially implemented, and network operators now have a huge opportunity to monetize new products and services for companies and customers. Network slicing is a critical tool for achieving customer service and assured reliability. Ericsson has created the most comprehensive network slicing platform, comprising 5G Radio Access Networks (RAN) slicing, enabling automatic and quick deployment of services of new and creative 5G use scenarios, using an edge strategy (Subedi et al., 2021). Ericsson 5G Radio Access Networks (RAN) Slicing has indeed been released, and telecom companies are enthusiastic about the possibilities of new 5G services. For mobile network operators, using system control to coordinate bespoke network slices in the personal and commercial market sectors can provide considerable income prospects. Ericsson provides unique procedures to ensure that speed and priority are maintained throughout the network slicing process. Not only do they have operational and business support systems (OSS/BSS), central, wireless, and transit systems in their portfolio, but they also have complete services like Network Support and Service Continuity (Debbabi, Jmal and Chaari Fourati, 2021).

What is 5G Radio Access Networks (RAN) Slicing?#

The concept of network slicing is incomplete without the cooperation of communication service providers. It assures that the 5G Radio Access Networks (RAN) Slicing-enabled services are both dependable and effective. Carriers can't ensure slice efficiency or meet service contracts unless they have network support and service continuity. Furthermore, if carriers fail to secure slice performance or meet the service-level agreement, they may face punishment and the dangers of losing clients (Mathew, 2020). Ericsson 5G Radio Access Networks (RAN) Slicing provides service operators with the unique and assured quality they have to make the most of their 5G resources. The novel approach was created to improve end-to-end network slicing capabilities for radio access network managing resources and coordination. As a consequence, it constantly optimizes radio resource allocation and priority throughout multiple slices to ensure service-level commitments are met. This software solution, which is based on Ericsson radio experience and has a flexible and adaptable design, will help service providers to satisfy expanding needs in sectors such as improved broadband access, network services, mission-critical connectivity, and crucial Internet of Things (IoT) (Li et al., 2017).

Ericsson Network Support#

Across complex ecosystems, such as cloud networks, Network Support enables data-driven fault isolation, which is also necessary to efficiently manage the complexity in [5G systems]. To properly manage the complexity of 5G networks, Ericsson Network Support offers data-driven fault isolation. This guarantees that system faults are quickly resolved and that networks are reliable and robust. Software, equipment, and replacement parts are divided into three categories in Network Support. By properly localizing defects and reducing catastrophic occurrences at the solution level, Ericsson can offer quick timeframes and fewer site visits. Ericsson also supports network slicing by handling multi-vendor ecosystem fault separation and resolving complications among domains (Zhang, 2019). Data-driven fault isolation from Ericsson guarantees the quick resolution of connection problems, as well as strong and effective networks, and includes the following innovative capabilities:

• Ericsson Network Support (Software) provides the carrier's software platform requirements across classic, automated, and cloud-based services in extremely sophisticated network settings. It prevents many mishaps by combining powerful data-driven support approaches with strong domain and networking experience.

• Ericsson Hardware Services provides network hardware support. Connected adds advanced technologies to remote activities, allowing for quicker problem identification and treatment. It integrates network data with past patterns to provide service personnel and network management with relevant real-time information. It is feasible to pinpoint errors with greater precision using remote scans and debugging.

• The Spare Components Management solution gives the operator's field engineers access to the parts they need to keep the network up and running (Subedi et al., 2021). Ericsson will use its broad network of logistical hubs and local parts depots to organize, warehouse, and transport the components.

Ericsson Service Continuity#

To accomplish 5G operational readiness, Service Continuity provides AI-powered, proactive assistance, backed by tight cooperation and Always-On service. Advanced analytical automation and reactive anticipatory insights provided by Ericsson Network Intelligence allow Service Continuity services. It focuses on crucial functionality to help customers reach specified business objectives while streamlining processes and ensuring service continuity (Katsalis et al., 2017). It is based on data-driven analysis and worldwide knowledge that is given directly and consists of two services:

• Ericsson Service Continuity for 5G: Enables the clients' networks to take remedial steps forward in time to prevent end-user disruption, allowing them to move from responsive to proactively network services.

• Ericsson Service Continuity for Private Networks is a smart KPI-based support product for Industry 4.0 systems and services that is targeted to the unique use of Private Networks where excellent performance is critical (Mathew, 2020).

Conclusion for 5G Network Slicing

Network slicing will be one of the most important innovations in the 5G network area, transforming the telecommunications sector. The 5G future necessitates a network that can accommodate a diverse variety of equipment and end customers. Communication service providers must act quickly as the massive network-slicing economic potential emerges (Da Silva et al., 2016). However, deciding where to begin or where to engage is difficult. Ericsson's comprehensive portfolio and end-to-end strategy include Network Support and Service Continuity services. Communication service providers across the world would then "walk the talk" for Network Slicing in the 5G age after incorporating them into their network operations plan.

References#

• Da Silva, I.L., Mildh, G., Saily, M. and Hailu, S. (2016). A novel state model for 5G Radio Access Networks. 2016 IEEE International Conference on Communications Workshops (ICC).
• Debbabi, F., Jmal, R. and Chaari Fourati, L. (2021). 5G network slicing: Fundamental concepts, architectures, algorithmics, project practices, and open issues. Concurrency and Computation: Practice and Experience, 33(20).
• Katsalis, K., Nikaein, N., Schiller, E., Ksentini, A. and Braun, T. (2017). Network Slices toward 5G Communications: Slicing the LTE Network. IEEE Communications Magazine, 55(8), pp.146–154.
• Li, X., Samaka, M., Chan, H.A., Bhamare, D., Gupta, L., Guo, C. and Jain, R. (2017). Network Slicing for 5G: Challenges and Opportunities. IEEE Internet Computing, 21(5), pp.20–27.
• Mathew, A., 2020, March. Network slicing in 5G and the security concerns. In 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC) (pp. 75-78). IEEE.
• Subedi, P., Alsadoon, A., Prasad, P.W.C., Rehman, S., Giweli, N., Imran, M. and Arif, S. (2021). Network slicing: a next-generation 5G perspective. EURASIP Journal on Wireless Communications and Networking, 2021(1).
• Zhang, S. (2019). An Overview of Network Slicing for 5G. IEEE Wireless Communications, [online] 26(3), pp.111–117.

Introduction#

Consumers want quicker, better, more convenient, and revolutionary data speeds in this internet age. Many people are eager to watch movies on their smartphones while also downloading music and controlling many IoT devices. They anticipate a 5G connection, which will provide 100 times quicker speeds, 10 times more capacity, and 10 times reduced latency. The transition to 5G necessitates significant expenditures from service providers. To support new income streams and enable better, more productive, and cost-effective processes and exchanges, BSS must advance in tandem with 5G network installations (Pablo Collufio, 2019). Let's get ready to face the challenges of 5G monetization.

Why 5G monetization?#

The appropriate 5G monetization solutions may be a superpower, allowing CSPs to execute on 5G's potential from the start. The commercialization of 5G is a hot topic. "Harnessing the 5G consumer potential" and "5G and the Enterprise Opportunity" are two studies that go through the various market prospects. They illustrate that, in the long term, there is a tremendous new income opportunity for providers at various implementation rates, accessible marketplaces, and industry specializations. “Getting creative with 5G business models” highlights how AR/VR gameplay, FWA (Fixed Wireless Access), and 3D video encounters could be offered through B2C, B2B, and B2B2X engagement models in a variety of use scenarios. To meet the 5G commitments of increased network speeds and spectrum, lower latency, assured service quality, connectivity, and adaptable offers, service suppliers must discuss their BSS evolution alongside their 5G installations, or risk being unable to monetize those new use cases when they become a real thing (Munoz et al., 2020). One of the abilities that will enable providers to execute on their 5G promises from day one is 5G monetization. CSPs must update their business support systems (BSS) in tandem with their 5G deployment to meet 5G use scenarios and provide the full promise of 5G, or risk slipping behind in the 5G race for lucrative 5G services (Rao and Prasad, 2018).

Development of the BSS architecture#

To fully realize the benefits of 5G monetization, service providers must consider the growth of their telecom BSS from a variety of angles:

• Integrations with the network - The new 5G Basic standards specify a 5G Convergent Charging System (CCS) with a 5G Charging Function (CHF) that enables merged charging and consumption limit restrictions in the new service-based architecture that 5G Core introduces.
• Service orchestration - The emergence of distributed systems and more business services need more complicated and stricter service coordination and fulfillment to ensure that goods, packages, ordeals, including own and third-party products, are negotiated, purchased, and activated as soon as clients require them.
• Expose - Other BSS apps, surrounding levels such as OSS and Central networks, or 3rd parties and collaborators who extend 5G services with their abilities might all be consumers of BSS APIs (Mor Israel, 2021).
• Cloud architecture - The speed, reliability, flexibility, and robustness required by 5G networks and services necessitate a new software architecture that takes into consideration BSS deployments in the cloud, whether private, public, or mixed.

Challenges to 5G Monetization#

Even though monetizing 5G networks appears to be a profitable prospect for telecommunications, it is not without flaws. The following are the major challenges:

• Massive upfront investments in IT infrastructure, network load, and a radio access system, among other things.
• To get optimal ROI, telecommunications companies must establish viable monetization alternatives (Bega et al., 2019).
• The commercialization of 5G necessitates a change in telecom operations.

Case of Augmented Reality Games and Intelligent Operations#

With the 5G Core, BSS, and OSS in place, it's time to bring on a new partner: a cloud gaming firm that wants to deliver augmented reality monetization strategies to the operator's users (Feng et al., 2020). For gaming traffic, they want a specific network slice with assured service quality. Through a digital platform, a member in a smart, completely automated network may request their network slice and specify their SLAs. BSS decomposes this order into multiple sub-orders, such as the construction and provisioning of the particular portion through the OSS, once it receives it. The operator additionally uses their catalog-driven design to describe the item offered that its customers will acquire to get onboard new on the partner's network slice all in one location. This deal is immediately disseminated to all relevant systems, including online charging, CRM, and digital platforms, and may be generally consumed.

Conclusion#

5G can impact practically every industry and society. Even though there is a lot of ambiguity around 5G and a lot of technical concerns that need to be resolved, one thing is certain: 5G is the next big thing. Finally, whenever a user buys a new plan, he or she is automatically onboarded in the particular portion, often without affecting the system. The partnership will be able to monitor the network health and quality of various types of services for each customer in real time and will be able to take immediate decisions or conduct promotions based on this data (Bangerter et al., 2014). New platforms may adapt to changes based on factual resource use thanks to the BSS cloud architecture. All information regarding purchases, items, network usage, and profitability, among other things, is given back into circulation and utilized as feeds for infrastructure and catalog design in a closed-loop method.

References#

• Bangerter, B., Talwar, S., Arefi, R., and Stewart, K. (2014). Networks and devices for the 5G era. IEEE Communications Magazine, 52(2), pp.90–96.
• Bega, D., Gramaglia, M., Banchs, A., Sciancalepore, V. and Costa-Perez, X. (2019). A Machine Learning approach to 5G Infrastructure Market optimization. IEEE Transactions on Mobile Computing, pp.1–1.
• Feng, S., Niyato, D., Lu, X., Wang, P. and Kim, D.I. (2020). Dynamic Game and Pricing for Data Sponsored 5G Systems With Memory Effect. IEEE Journal on Selected Areas in Communications, 38(4), pp.750–765.
• Mor Israel (2021). How BSS can enable and empower 5G monetization. online Available at: https://www.ericsson.com/en/blog/2021/4/how-bss-can-enable-and-empower-5g-monetization.
• Munoz, P., Adamuz-Hinojosa, O., Navarro-Ortiz, J., Sallent, O. and Perez-Romero, J. (2020). Radio Access Network Slicing Strategies at Spectrum Planning Level in 5G and Beyond. IEEE Access, 8, pp.79604–79618.
• Pablo Collufio, D. (2019). 5G: Where is the Money? e-archivo.uc3m.es. online.
• Rao, S.K. and Prasad, R. (2018). Telecom Operators’ Business Model Innovation in a 5G World. Journal of Multi Business Model Innovation and Technology, 4(3), pp.149–178.

Learn more about Edge Computing and its usage in different fields. Keep reading our blogs.

Edge VMs And Edge Containers are nothing but VMs and Containers used in Edge Locations, or are they different? This topic gives a brief insight into it.

Introduction

If you have just recently begun learning about virtualization techniques, you could be wondering what the distinctions between containers and VMs. The issue over virtual machines vs. containers is at the centre of a discussion over conventional IT architecture vs. modern DevOps approaches. Containers have emerged as a formidable presence in cloud-based programming, thus it's critical to know what they are and isn't. While containers and virtual machines have their own set of features, they are comparable in that they both increase IT productivity, application portability, and DevOps and the software design cycle (Zhang et al., 2018). The majority of businesses have adopted cloud computing, and it has shown to be a success, with significantly faster workload launches, simpler scalability and flexibility, and fewer hours invested on underlying traditional data centre equipment. Traditional cloud technology, on the other hand, isn't ideal in every case.

Microsoft Azure, Amazon AWS, and Google Cloud Platform (GCP) are all traditional cloud providers with data centres all around the world. Whereas each company's data centre count is continually growing, these data centres are not near enough to consumers when an app requires optimal speed and low lag (Li and Kanso, 2015). Edge computing is useful when speed is important or produced data has to be kept near to the consumers.

What is the benefit of Edge Computing?#

Edge computing is a collection of localized mini data centres that relieve the cloud of some of its responsibilities, acting as a form of "regional office" for local computing chores rather than transmitting them to a central data centre thousands of miles away. It's not meant to be a replacement for cloud services, but rather a supplement. Instead of sending sensitive data to a central data centre, edge computing enables you to analyse it at its origin (Khan et al., 2019). Minimal sensitive data is sent across devices and the cloud, which means greater security for both you and your users. Most IoT initiatives may also be completed at a lower cost by decreasing data transit and storage space using traditional techniques.

What are Edge VMs?#

Edge virtual machines (Edge VMs) are technological advancements of standard VM in which the storage and computation capabilities that support the VM are physically closer to the end-users. Each VM is a self-contained entity with its OS, capable of handling almost any program burden (Millhouse, 2018). The flexibility, adaptability, and optimum availability of such tasks are significantly improved by VM designs. Patching, upgrades, and care of the virtual machine's operating system are required regularly. Monitoring is essential for ensuring the virtual machine instances' and underpinning physical hardware infrastructure's stability. Backup and data recovery activities must also be considered. All of this adds up to a lot of time spent on repair and supervision.

What are Edge Containers?#

Edge containers are decentralized computing capabilities that are placed as near to the end customer as feasible in an attempt to decrease delay, conserve data, and improve the overall user experiences. A container is a sandboxed, isolated version of a component of a programme. Containers still enable flexibility and adaptability, although usually isn't for every container in an application framework, only for the one that needs expanding (Pahl and Lee, 2015). It's simple to reboot multiple copies of a container image and bandwidth allocation between them once you've constructed one.

What's the difference Between VMs and Containers even without the context Edge?#

Containers are perfect where your programme supports a microservices design, which allows application programs to function and scale freely. Containers may operate anywhere as long as your public cloud or edge computing platform has a Docker engine (Sharma et al., 2016). Also, there is a reduction in operational and administrative costs. But when your application requires particular operating system integration that is not accessible in a container, VM is still suggested when you need access to the entire OS. VMs are required if you want or need additional control over the software architecture, or if you want or need to execute many apps on the same host.

Next Moves#

Edge computing is a viable solution for applications that require high performance and low latency communication. Gaming, broadcasting, and production are all common options. You may deliver streams of data from near to the user or retain data close to the source, which is more convenient than using open cloud data centres (Sonmez, Ozgovde and Ersoy, 2018). You can pick what is suitable for your needs now that you know more about edge computing, including the differences between edge VMs and edge containers.

Learn more about Edge Computing and its usage in different fields - Nife Blogs

Introduction#

The gaming business, which was formerly considered a specialized sector, has grown to become a giant $120 billion dollar industry in the latest years (Scholz, 2019). The gaming business has long attempted to capitalize on new possibilities and inventive methods to offer gaming adventures, as it has always been the leading result of technology. The emergence of cloud gaming services is one of the most exciting advances in cloud computing technology in recent years. To succeed, today's gamers speed up connections. Fast connectivity contributes to improved gameplay. Gamers may livestream a collection of games on their smartphone, TV, platform, PC, or laptop for a monthly cost ranging from $10 to $35 (Beattie, 2020).

Reasons to buy a gaming computer:

• The gameplay experience is second to none.
• Make your gaming platform future-proof.
• They're prepared for VR.
• Modified versions of your favourite games are available to play.
• More control and better aim.

Why is Hardware PC gaming becoming more popular?#

Gamers are stretching computer hardware to its boundaries to get an edge. Consoles like the PlayStation and Xbox are commonplace in the marketplace, but customers purchasing pricey gaming-specific PCs that give a competitive advantage over the other gamers appear to be the next phenomenon. While the pull of consoles remains strong, computer gaming is getting more and more popular. It was no longer only for the die-hards who enjoy spending a weekend deconstructing their computer. A gaming PC is unrivalled when it comes to providing an unrivalled gaming experience. It's incredible to think that gamers could play the newest FPS games at 60fps or greater. Steam is a global online computer gaming platform with 125 million members, compared to 48 million for Xbox Live (Galehantomo P.S, 2015). Gaming computers may start around $500 and soon grow to $1500 or more, which is one of the most significant drawbacks of purchasing gaming PCs.

The majority of games are now downloadable and played directly on cell phones, video game consoles, and personal computers. With over 3 billion gamers on the planet, the possibility and effect might be enormous (Wahab et al., 2021). Cloud gaming might do away with the need for dedicated platforms, allowing players to play virtually any game on practically any platform. Users' profiles, in-game transactions, and social features are all supported by connectivity, but the videogames themselves are played on the gamers' devices. Gaming has already been growing into the cloud in this way for quite some time. Every big gaming and tech firm seems to have introduced a cloud gaming service in the last two years, like Project xCloud by Microsoft, PlayStation Now by Sony, and Stadia by Google.

Cloud Computing's Advantages in the Gaming World:

• Security
• Compatibility
• Cost-effective
• Accessibility
• No piracy
• Dynamic support

What are Cloud Gaming Services, and how do they work?#

Cloud gaming shifts the processing of content from the user's device to the cloud. The game's perspective is broadcast to the person's devices through content delivery networks with local stations near population centres, similar to how different channels distribute the material. Size does matter, just like it does with video. A modest cell phone screen can show a good gaming feed with far fewer bits than a 55" 4K HDTV. In 2018, digital downloads accounted for more than 80% of all video game sales. A bigger stream requires more data, putting additional strain on the user's internet connection. Cloud streaming services must automatically change the bandwidth to offer the lowest amount of bits required for the best service on a specific device to control bandwidth (Cai et al., 2016).

Edge Gaming - The appeal of Edge Computing in Gaming#

Revenue from mobile gaming is growing more sociable, engaging, and dynamic. As games become more collaborative, realistic, and engaging, mobile gaming revenue is predicted to top $95 billion worth by 2022 (Choy et al., 2014). With this growth comes the difficulty of meeting consumers' desire for ultra-fast, low-latency connectivity, which traditional data centres are straining to achieve. Edge computing refers to smaller data centres that provide cloud-based computational services and resources closer to customers or at the network's edge. In smartphone games, even just a fraction of a millisecond of latency would be enough to completely ruin the gameplay. Edge technology and 5G connection assist in meeting low-latency, high-bandwidth needs by bringing high cloud computing power directly to consumers and equipment while also delivering the capacity necessary for high, multi-player gameplay.

Issues with Cloud Gaming#

Cloud technology isn't only the future of gaming it's also the future of hybridized multi-clouds and edge architecture as a contemporary internet infrastructure for businesses. However, this cutting-edge technology faces a few obstacles. Lag, also known as latency, is a delay caused by the time required for a packet of data to move from one place in a network to another. It's the misery of every online gamer's existence. Streaming video sputters, freezes, and fragments due to high latency networks (Soliman et al., 2013). While this might be frustrating when it comes to video material, it can be catastrophic when it comes to cloud gaming services.

Developers are Ready for the Change#

Gaming is sweeping the media landscape. Please have a look around if you are unaware of this information. Although cloud gameplay is still in its infancy, it serves as proof that processing can be done outside of the device. I hope that cloud gaming is treated as the proving point that it is. Because cloud gameplay always has physical issues, we should look to edge gaming to deliver an experience where gamers can participate in a real-time multiplayer setting.

References#

• https://www.investopedia.com/articles/investing/053115/how-video-game-industry-changing.asp
• Beattie, A. (2020). How the Video Game Industry Is Changing. [online] Investopedia. Available at:
• Cai, W., Shea, R., Huang, C.-Y., Chen, K.-T., Liu, J., Leung, V.C.M. and Hsu, C.-H. (2016). The Future of Cloud Gaming . Proceedings of the IEEE, 104(4), pp.687-691.
• Choy, S., Wong, B., Simon, G. and Rosenberg, C. (2014). A hybrid edge-cloud architecture for reducing on-demand gaming latency. Multimedia Systems, 20(5), pp.503-519.
• Galehantomo P.S, G. (2015). Platform Comparison Between Games Console, Mobile Games And PC Games. SISFORMA, 2(1), p.23.
• Soliman, O., Rezgui, A., Soliman, H. and Manea, N. (2013). Mobile Cloud Gaming: Issues and Challenges. Mobile Web Information Systems, pp.121-128.
• Scholz, T.M. (2019). eSports is Business Management in the World of Competitive Gaming. Cham Springer International Publishing.
• Wahab, A., Ahmad, N., Martini, M.G. and Schormans, J. (2021). Subjective Quality Assessment for Cloud Gaming. J, 4(3), pp.404-419.

Nife started off as an edge computing deployment platform but has moved away to multi-cloud- a hybrid cloud setup

Collated below is some news about Nife and the Platform

• Read about the story of Nife, how it all started and the future months https://yourstory.com/herstory/2021/10/woman-entrepreneur-cloud-edge-computing-nife-labs/amp
• Read about the story of edge computing with Nife and what is in store in the next few months. https://yourstory.com/2021/06/nifes-founder-share-businesses-must-take-note-edge-computing/amp
• Learnings from Pandemic and more - Antler startups talk about https://www.antler.co/blog/the-good-the-bad-and-the-unexpected-what-entrepreneurs
• Nife is a startup showcasing at the Antler Demo Dayhttps://kr-asia.com/these-are-the-14-startups-ready-for-antlers-southeast-asia-demo-day
• Know how Virtualization and AI Acceleration on cloud and we effectively used Edge in robotics https://link.springer.com/chapter/10.1007/978-981-16-2937-2_10
• 5G offers a unique opportunity for advertisers to serve contextual advertisements in ways never seen before. Read this article to know more about this topic to design your advertisement more efficiently https://wiprotechblogs.medium.com/contextual-ads-winning-in-a-5g-world-94dee05b871d

• Developing Pagemark for Edge Computing. The value of edge computing comes from the data and technologies that adapt to evolving needs.
• https://tecknexus.com/5g-network/developing-pagerank-for-edge-computing/
• https://yourstory.com/2021/06/nifes-founder-share-businesses-must-take-note-edge-computing/amp

• Finalists of Qualcomm Wommen Entrepreneur India Network (QWEIN) 2021 [Nife] Labs was one of the finalists out of the 12 Startups listed https://yourstory.com/2021/08/qualcomm-women-entrepreneur-india-finalists/amp
• The 5G smart Railway Hackathon Won by 'Nife Labs'Featured in Yourstory about Why Businesses Must Take Note of Edge Computing https://www.startupleadership.com/blog/rec1283/
• Quotes on Digital Transformation https://startupnewsxp.blogspot.com/2021/07/when-ecosystems-become-digital-there.html
• 5G Smart Railway Hackathon. This Hackathon program was part of the 5g Railway Lab in Barcelona and it was fully designed and facilitated by the WorldStratup Lab Unit
• Nife at NetApp Demo Day announcement https://yourstory.com/2021/09/netapp-excellerators-demo-day-unrestrained-startups-spirit/amp
• Nife joins the 8th Cohort of NetApp Excellarator - https://yourstory.com/2021/05/netapp-excellerator-cohort-8-8-startups-using-data-cloud/amp

Learn more about different use cases on edge computing- Nife Blogs

Contextual Ads at Edge are buzzing around the OTT platforms. To achieve the perfect mix of customer experience and media monetization, advertisers will need a technology framework that harnesses various aspects of 5G, such as small cells and network slicing, to deliver relevant content in real time with zero latency and lag-free advertising.

Why Contextual Ads at Edge?#

"In advertising, this surge of data will enable deeper insights into customer behaviors and motivations, allowing companies to develop targeted, hyper-personalized ads at scale — but just migrating to 5G is not enough to enable these enhancements. To achieve the perfect mix of customer experience and media monetization, advertisers will need a technology framework that harnesses various aspects of 5G, such as small cells and network slicing, to deliver relevant content in real-time with zero latency and lag-free advertising."

Contextual Video Ads Set to Gain#

A recent study shows that 86% of businesses used videos as their core marketing strategy in 2021 compared to 61% in 2016. A report by Ericsson estimates videos will account for 77% of mobile data traffic by 2025 versus 66% currently.

Read more about Contextual Ads at Edge in the article covered by Wipro.

Wipro Tech Blogs - Contextual Ads Winning in a 5G World

Are you familiar with the differences between edge computing and cloud computing? Is edge computing a type of branding for a cloud computing resource, or is it something new altogether? Let us find out!

The speed with which data is being added to the cloud is immense. This is because the growing number of devices in the cloud are centralized, so it must transact the information from where the cloud servers are, hence data needs to travel from one location to another so the speed of data travel is slow. If this transaction starts locally, then the data travels at a shorter distance, making it faster. Therefore, cloud suppliers have combined Internet of Things strategies and technology stacks with edge computing for the best usage and efficiency.

In the following article, we will understand the differences between cloud and edge computing. Let us see what this is and how this technology works.

EDGE COMPUTING#

Edge Computing is a varied approach to the cloud. It is the processing of real-time data close to the data source at the edge of any network. This means applications close to the data generated instead of processing all data in a centralized cloud or a data center. It increases efficiency and decreases cost. It brings the storage and power closer to the device where it is most needed. This distribution eliminates lag and saves a scope for various other operations.

It is a networking system, within which data servers and data processing are closer to the computing process so that the latency and bandwidth problems can be reduced.

Now that we know what the basics of edge computing are, let's dive in a little deeper for a better understanding of terms commonly associated with edge computing:

Latency#

Latency is the delay in contacting in real-time from a remotely located data center or cloud. If you are loading an image over the internet, the time to show up completely is called the latency time.

Bandwidth#

The frequency of the maximum amount of data sent over an Internet connection at a time is called Bandwidth. We refer to the speed of sent and received data over a network that is calculated in megabits per second or MBPS as bandwidth.

Leaving latency and bandwidth aside, we choose edge computing over cloud computing in hard-to-reach locations, where there is limited or no connectivity to a central unit or location. These remote locations need local computing, and edge computing provides the perfect solution for it.

Edge computing also benefits from specialized and altered device functions. While these devices are like personal computers, they are not regular computing devices and perform multiple functions benefiting the edge platform. These specialized computing devices are intelligent and respond to machines specifically.

Benefits of Edge Computing#

• Gathering data, analyzing, and processing is done locally on host devices on the edge of the network, which has the caliber to be completed within a fraction of a second.

• It brings analytical capabilities comparatively closer to the user devices and enhances the overall performance.

• Edge computing is a cheaper alternative to the cloud as data transfer is a lengthy and expensive process. It also decreases the risk involved in transferring sensitive user information.

• Increased use of edge computing methods has transformed the use of artificial intelligence in autonomous driving. Artificial Intelligence-powered and self-driving cars and other vehicles require massive data presets from their surroundings to function perfectly in time. If we use cloud computing in such a case, it would be a dangerous application because of the lag.

• The majority of OTT platforms and streaming service providers like Netflix, Amazon Prime, Hulu, and Disney+ to name a few, create a heavy load on cloud network infrastructure. When popular content is cached closer to the end-users in storage facilities for easier and quicker access. These companies make use of the nearby storage units close to the end-user to deliver and stream content with no lag if one has a stable network connection.

The process of edge computing varies from cloud computing as the latter takes considerably more time. Sometimes it takes up to a couple of seconds to channel the information to the data centers, ultimately resulting in delays in crucial decision-making. The signal latency can translate to huge losses for any organization. So, organizations prefer edge computing to cloud computing which eliminates the latency issue and results in the tasks being completed in fractions of a second.

CLOUD COMPUTING#

A cloud is an information technology environment that abstracts, pools, and shares its resources across a network of devices. Cloud computing revolves around centralized servers stored in data centers in large numbers to fulfill the ever-increasing demand for cloud storage. Once user data is created on an end device, its data travels to the centralized server for further processing. It becomes tiresome for processes that require intensive computations repeatedly, as higher latency hinders the experience.

Benefits of Cloud Computing#

• Cloud computing gives companies the option to start with small clouds and increase in size rapidly and efficiently as needed.

• The more cloud-based resources a company has, the more reliable its data backup becomes, as the cloud infrastructure can be replicated in case of any mishap.

• There is little to no service cost involved with cloud computing as the service providers conduct system maintenance on their own from time to time.

• Cloud enables companies to help cut expenses in operational activities and enables mobile accessibility and user engagement framework to a higher degree.

• Many mainstream technology companies have benefited from cloud computing as a resourceful platform. Slack, an American cloud-based software as a service, has hugely benefited from adopting cloud servers for its application of business-to-business and business-to-consumer commerce solutions.

• Another largely known technology giant, Microsoft has its subscription-based product line ‘Microsoft 365' which is centrally based on cloud servers that provide easy access to its office suite.

• Dropbox, infrastructure as a service provider, provides a service- cloud-based storage and sharing system that runs solely on cloud-based servers, combined with an online-only application.

KEY DIFFERENCES#

• The main difference between edge computing and cloud computing is in data processing within the case of cloud computing, data travel is long, which causes data processing to be slower but in contrast edge computing reduces the time difference in the data processing. It's essential to have a thorough understanding of the working of cloud and edge computing.

• Edge computing is based on processing sensitive information and data, while cloud computing processes data that is not time constrained and uses a lesser storage value. To carry out this type of hybrid solution that involves both edge and cloud computing, identifying one's needs and comparing them against monetary values must be the first step in assessing what works best for you. These computing methods vary completely and comprise technological advances unique to each type and cannot replace each other.

• The centralized locations for edge computing need local storage, like a mini data center. Whereas, in the case of cloud computing, the data can be stored in one location. Even when used as part of manufacturing, processing, or shipping operations, it is hard to co-exist without IoT. This is because everyday physical objects that collect and transfer data or dictate actions like controlling switches, locks, motors, or robots are the sources and destinations that edge devices process and activate without depending upon a centralized cloud.

With the Internet of Things gaining popularity and pace, more processing power and data resources are being generated on computer networks. Such data generated by IoT platforms is transferred to the network server, which is set up in a centralized location.

The big data applications that benefit from aggregating data from everywhere and running it through analytics and machine learning to prove to be economically efficient, and hyper-scale data centers will stay in the cloud. We chose edge computing over cloud computing in hard-to-reach locations, where there is limited connectivity to a cloud-based centralized location setup.

CONCLUSION#

The edge computing and cloud computing issue does not conclude that deducing one is better than the other. Edge computing fills the gaps and provides solutions that cloud computing does not have the technological advancements to conduct. When there is a need to retrieve chunks of data and resource-consuming applications need a real-time and effective solution, edge computing offers greater flexibility and brings the data closer to the end user. This enables the creation of a faster, more reliable, and much more efficient computing solution.

Therefore, both edge computing and cloud computing complement each other in providing an effective response system that is foolproof and has no disruptions. Both computing methods work efficiently and in certain applications, edge computing fills and fixes the shortcomings of cloud computing with high latency, fast performance, data privacy, and geographical flexibility of operations.

Functions that are best managed by computing between the end-user devices and local networks are managed by the edge, while the data applications benefit from outsourcing data from everywhere and processing it through AI and ML algorithms. The system architects who have learned to use all these options together have the best advantage of the overall system of edge computing and cloud computing.

Learn more about different use cases on edge computing-

Condition-based monitoring - An Asset to equipment manufacturers (nife.io)

Large-scale manufacturing units, especially industrial setups, have complicated equipment. Condition-based monitoring at the edge is unprecedented. Can this cost be reduced?

Learn More!

Background#

The world is leaning toward the Industrial 4.0 transformation, and so are the manufacturers. The manufacturers are moving towards providing services rather than selling one-off products. Edge computing in manufacturing is used to collect data, manage the data, and run the analytics. It becomes essential to monitor assets, check for any faults, and predict any issues with the devices. Real-time data analysis of assets detects faults so we can carry out maintenance before the failure of the system occurs. We can recognize all the faulty problems with the equipment. Hence, we need condition-based monitoring.

Why Edge Computing for Condition-Based Monitoring?#

Edge computing is used to collect data and then label it, further manage the data, and run the system's analytics. Then, we can send alerts to the end enterprise customer and the OEM to notify them when maintenance service is required. Using network edge helps eliminate the pain of collecting data from many disparate systems or machines.

The device located close to the plants or at the edge of the network provides condition-based monitoring, preempts early detection, and correction of designs, ensuring greater productivity for the plant.

Key Challenges and Drivers of Condition-Based Monitoring at Edge#

• Device Compatibility
• Flexibility in Service
• Light Device Support
• Extractive Industries

Solution#

To detect machinery failures, the equipment has a layer of sensors. These sensors pick up the information from the devices and pass it to a central processing unit.

Here, edge computing plays a crucial part in collecting and monitoring via sensors. The data from the sensors help the OEM and the system administrators monitor the exact device conditions, reducing the load on the end device itself. This way, administrators can monitor multiple sensors together. With the generation of the events, failure on one device can be collated with another device.

Edge also allows processing regardless of where the end device is located or if the asset moves. The same application can be extended to other locations. Alternatively, using edge helps remove the pain of collecting data from many disparate systems/machines in terms of battery.

The edge computing system based on conditions is used to collect statistics, manage the data, and run the analytics without any software hindrance. A system administrator can relax as real-time data analysis detects faults to carry out maintenance before any failure occurs.

Condition-based monitoring can be used in engineering and construction to monitor the equipment. Administrators can use edge computing industrial manufacturing for alerts and analytics.

On-Prem vs. Network Edge#

Given that the on-prem edge is lightweight, it's easy to place anywhere on the location. On the other hand, installing a device is overridden if the manufacturing unit decides to go with the network edge; hence, flexibility is automatically achieved.

How Does Nife Help with Condition-Based Monitoring at Edge?#

Use Nife as a network edge device to compute and deploy applications close to the industries.

Nife works on collecting sensor information, collating it, and providing immediate response time.

Benefits and Results#

• No difference in application performance (70% improvement from Cloud)
• Reduce the overall price of the Robots (40% Cost Reduction)
• Manage and monitor all applications in a single pane of glass
• Seamlessly deploy and manage navigation functionality (5 min to deploy, 3 min to scale)

Edge computing is an asset to different industries, especially device manufacturers, helping them reduce costs, improve productivity, and ensure that administrators can predict device failures.

You might like to read through this interesting topic of Edge Gaming!

Computer Vision at Edge is a growing subject with significant advancement in the new age of surveillance. Surveillance cameras can be primary or intelligent, but Intelligent cameras are expensive. Every country has some laws associated with Video Surveillance.

How do Video Analytics companies rightfully serve their customers, with high demand?

Nife helps with this.

Introduction#

The need for higher bandwidth and low latency processing has continued with the on-prem servers. While on-prem servers provide low latency, they do not allow flexibility.

Computer Vision can be used for various purposes such as Drone navigation, Wildlife monitoring, Brand value analytics, Productivity monitoring, or even Package delivery monitoring can be done with the help of these high-tech devices. The major challenge in computing on the cloud is data privacy, especially when images are analyzed and stored.

Another major challenge is spinning up the same algorithm or application in multiple locations, which means hardware needs to be deployed there. Hence scalability and flexibility are the key issues. Accordingly, Computing and Computed Analytics are hosted and stored in the cloud.

On the other hand, managing and maintaining the on-prem servers is always a challenge. The cost of the servers is high. Additionally, any device failure adds to the cost of the system integrator.

Thereby, scaling the application to host computer vision on the network edge significantly reduces the cost of the cloud while providing flexibility of the cloud.

Key Challenges and Drivers of Computer Vision at Edge#

• On-premise services
• Networking
• Flexibility
• High Bandwidth
• Low-Latency

Solution Overview#

Computer Vision requires high bandwidth and high processing, including GPUs. The Edge Cloud is critical in offering flexibility and a low price entry point of cloud hosting and, along with that, offering low latency necessary for compute-intensive applications.

Scaling the application to host on the network edge significantly reduces the camera's cost and minimizes the device capex. It can also help scale the business and comply with data privacy laws, e.g. HIPAA, GDPR, and PCI, requiring local access to the cloud.

How does Nife Help with Computer Vision at Edge?#

Use Nife to seamlessly deploy, monitor, and scale applications to as many global locations as possible in 3 simple steps. Nife works well with Computer Vision.

• Seamlessly deploy and manage navigation functionality (5 min to deploy, 3 min to scale)
  • No difference in application performance (70% improvement from Cloud)
  • Manage and Monitor all applications in a single pane of glass.
  • Update applications and know when an application is down using an interactive dashboard.
  • Reduce CapEx by using the existing infrastructure.

A Real-Life Example of the Edge Deployment of Computer Vision and the Results#

In the current practice, deploying the same application, which needs a low latency use case, is a challenge.

• It needs man-hours to deploy the application.
• It needs either on-prem server deployment or high-end servers on the cloud.

Nife servers are present across regions and can be used to deploy the same applications and new applications closer to the IoT cameras in Industrial Areas, Smart Cities, Schools, Offices, and in various locations. With this, you can monitor foot-fall, productivity, and other key performance metrics at lower costs and build productivity.

Conclusion#

Technology has revolutionized the world, and devices are used for almost all activities to monitor living forms. The network edge lowers latency, has reduced backhaul, and supports flexibility according to the user's choice and needs. We can attribute IoT cameras to scalability and flexibility, which are critical for the device. Hence, ensuring that mission-critical monitoring would be smarter, more accurate, and more reliable.

Want to know how you can save up on your cloud budgets? Read this blog.

AR Mirrors or Smart mirrors, the future of mirrors, is known as the world's most advanced Digital Mirrors. Augmented Reality mirrors are a reality today, and they hold certain advantages amidst COVID-19 as well.

Learn More about how to deploy and scale Smart Mirrors.

Introduction#

AR Mirrors are the future and are used in many places for ease of use for the end-users. AR mirrors are also used in Media & Entertainment sectors because the customers get easy usage of these mirrors, the real mirrors. The AI improves the edge's performance, and the battery concern is eradicated with edge computing.

Background#

Augmented Reality, Artificial intelligence, Virtual reality and Edge computing will help to make retail stores more interactive and the online experience more real-life, elevating the customer experience and driving sales.

Recently, in retail markets, the use of AR mirrors has emerged, offering many advantages. The benefits of using these mirrors are endless, and so is the ability of the edge.

For shoppers to go back to the stores, the touch and feel are the last to focus on. Smart Mirrors bring altogether a new experience of visualizing different garments, how the clothes actually fit on the person, exploring multiple choices and sizes to create a very realistic augmented reflection, yet avoiding physical wear and touch.

About#

We use real mirrors in trial rooms to try clothes and accessories. Smart mirrors have become necessary with the spread of the pandemic.

The mirrors make the virtual objects tangible and handy, which provides maximum utility to the users building on customer experience. Generally, as human nature, the normal mirrors in the real world more often to get a look and feel.

Hence, these mirrors take you to the virtual world, help you with looking at jewellery, accessories and even clothes making the shopping experience more holistic.

Smart Mirrors use an embedded processor with AI. The local processor ensures no lag when the user is using the Mirrors and hence provides an inference closest to the user. While this helps with the inference, the cost of the processor increases.

In order to drive large scale deployment, the cost of mirrors needs to be brought down. Today, AR mirrors have a high price, hence deploying them in retail stores or malls has become a challenge.

The other challenge includes updates to the AR application itself. Today, the System Integrator needs to go to every single location and update the application.

Nife.io delivers by using minimum unit architecture, each connected to the central edge server that can lower the overall cost and help to scale the application on Smart Mirror

Key challenges and drivers of AR Mirrors#

• Localized Data Processing
• Reliability
• Application performance is not compromised
• Reduced Backhaul

Result#

AR Mirrors deliver a seamless user experience with AI. It is a light device that also provides data localization for ease of access to the end-user.

AR Mirrors come with flexible features and can easily be used according to the user's preference.

Here, edge computing helps in reducing hardware costs and ensures that the customers and their end-users do not have to compromise with application performance.

1. The local AI processing moves to the central server.
2. The processor now gets connected to a camera to get the visual information and pass it on to the server.

Since the processing is moved away from the server itself, this helps AR mirrors also can help reduce battery life.

The critical piece here is lag in operations. The end-user should not face any lag, the central server then must have enough processing power and enough isolations to run the operations.

Since the central server with network connectivity is in the control of the application owner and the system integrator, the time spent to deploy in multiple servers is completely reduced.

How does Nife Help with AR Mirrors?#

Use Nife to offload device compute and deploy applications close to the Smart Mirrors.

• Offload local computation
• No difference in application performance (70% improvement from Cloud)
• Reduce the overall price of the Smart Mirrors (40% Cost Reduction)
• Manage and Monitor all applications in a single pane of glass.
• Seamlessly deploy and manage applications ( 5 min to deploy, 3 min to scale)

For scaling the robots, the biggest challenge is management and deployment. Robots have brought a massive change in the present era, and so we expect them to change the next generation. While it may not be true that the next generation of robotics will do all human work, robotic solutions help with automation and productivity improvements. Learn more!

Introduction#

In the past few years, we have seen a steady increase and adoption of robots for various use-cases. When industries use robots, multiple robots perform similar tasks in the same vicinity. Typically, robots consist of embedded AI processors to ensure real-time inference, preventing lags.

Robots have become integral to production technology, manufacturing, and Industrial 4.0. These robots need to be used daily. Though embedded AI accelerates inference, high-end processors significantly increase the cost per unit. Since processing is localized, battery life per robot also reduces.

Since the robots perform similar tasks in the same vicinity, we can intelligently use a minimal architecture for each robot and connect to a central server to maximize usage. This approach aids in deploying robotics, especially for Robotics as a Service use-cases.

The new architecture significantly reduces the cost of each robot, making the technology commercially scalable.

Key Challenges and Drivers for Scaling Deployment of Robotics#

• Reduced Backhaul
• Mobility
• Lightweight Devices

How and Why Can We Use Edge Computing?#

Device latency is critical for robotics applications. Any variance can hinder robot performance. Edge computing can help by reducing latency and offloading processing from the robot to edge devices.

Nife's intelligent robotics solution enables edge computing, reducing hardware costs while maintaining application performance. Edge computing also extends battery life by removing high-end local inference without compromising services.

Energy consumption is high for robotics applications that use computer vision for navigation and object recognition. Traditionally, this data cannot be processed in the cloud; hence, embedded AI processors accelerate transactions.

Virtualization and deploying the same image on multiple robots can also be optimized.

We enhance the solution's attractiveness to end-users and industries by reducing costs, offloading device computation, and improving battery life.

Solution#

Robotics solutions are valuable for IoT, agriculture, engineering and construction services, healthcare, and manufacturing sectors.

Logistics and transportation are significant areas for robotics, particularly in shipping and airport operations.

Robots have significantly impacted the current era, and edge computing further reduces hardware costs while retaining application performance.

How Does Nife Help with Deployment of Robotics?#

Use Nife to offload device computation and deploy applications close to the robots. Nife works with Computer Vision.

• Offload local computation
• Maintain application performance (70% improvement over cloud)
• Reduce robot costs (40% cost reduction)
• Manage and Monitor all applications in a single interface
• Seamlessly deploy and manage navigation functionality (5 minutes to deploy, 3 minutes to scale)

A Real-Life Example of Edge Deployment and the Results#

In this customer scenario, robots were used to pick up packages and move them to another location.

If you would like to learn more about the solution, please reach out to us!

What's happening with 5G and the 5G networks innovation and rollout? How are these shaping innovation and the world we know? Are you curious? Read More!

We will never forget the year 2020 as the year of the COVID-19 pandemic. We all remember how we witnessed a lengthy lockdown during 2020, and it put all our work on a halt for some time. But we all know that the internet remains one of the best remedies to spend time while at home. We have a 4G network, but there was news that the 5G network would soon become a new normal. Interestingly, even during COVID-19, there were several developments in the 5G network. This article will tell you how the 5G network testing and development stayed intact even during the pandemic.

Innovative Tools That Helped in 5G Testing Even During the Pandemic (Intelligent Site Engineering)#

To continue the 5G testing and deployment even during a pandemic, Telcos used specific innovative tools, the prominent being ISE (Intelligent Site Engineering).

What is Intelligent Site Engineering?#

Intelligent Site Engineering refers to the technique of using laser scanners and drones to design network sites. It is one of the latest ways of network site designing. In this process, they collected every minute detail to create a digital twin of a network site. If the company has a digital twin of a network site, they can operate it anywhere, virtually.

They developed Intelligent Site Engineering to meet the increasing data traffic needs and solve the network deployment problems of the Communication Service Providers (CSPs). This incredible technology enabled the site survey and site design even during the pandemic. We all know that site design and site surveys are vital for the proper installation of a network. But it was not possible to survey the site physically. Therefore, these companies used high technologies to launch and deploy 5G networks even in these lockdown times.

Intelligent Site Engineering uses AI (Artificial Intelligence) and ML (Machine Learning) to quickly and efficiently deliver a network. This helps CSPs to deploy frequency bands, multiple technologies, and combined topologies in one place. This advanced technology marks the transition from the traditional technique of using paper, pen, and measuring tape for a site survey to the latest styles like drones carrying high-resolution cameras and laser scanning devices.

How Does Intelligent Site Engineering Save Time?#

The Intelligent Site Engineering technique saves a lot of time for CSPs. For example, in this digitized version, CSPs take only 90 minutes for a site survey. Previously, they had to waste almost half a day in site surveys using primitive tools in the traditional method. Instead, the engineers can use the time these service providers save for doing other critical work.

Also, this process requires fewer people because of digitalization. This means that it saves the headcount of the workforce and the commuting challenges. Also, it reduces the negative impact on the environment.

What is the Use of Digital Twins Prepared in This Process?#

Using Intelligent Site Engineering, the CSPs replicate the actual site. They copied digital twins through 3D scans and photos clicked from every angle. The engineers then use the copies to get an accurate analysis of the site data. With the highly accurate data, they prepared the new equipment. The best example of digital twins is that CSPs can make a wise decision regarding altering the plan for future networks. Therefore, a digital twin comes in handy, from helping in creating material bills to detailed information about the networking site and related documents.

The technique is helpful for customers as well. For example, through the digital twin, customers can view online documents and sign them. In this way, this advanced technology and innovation enable remote acceptance of network sites even with 5G.

How Did the COVID Pandemic Promote the Digitalization of the 5G Network?#

We all know that the COVID pandemic and the subsequent lockdown put several restrictions on travel. Since no one could commute to the network site, it prepared us to switch to digital methods for satisfying our needs. The result was that we switched to Intelligent Site Engineering for 5G network deployment, bringing in 5G networks innovation.

When physical meetings were restricted, we switched to virtual conversations. Video meetings and conference calls became a new normal during the pandemic. Therefore, communication service providers also used the screen share features to show the clients the network sites captured using drones and laser technology. The image resolution was excellent, and the transition from offline to online mode was successful. Training of the personnel also became digitized.

The best part of this digitalization was that there was no need to have everyone on one site. Using these digital twins and technological tools, anyone can view those designs from anywhere. The companies could share the screen, and the clients could review the site without physical presence.

How Much Efficiency Were the CSPs Able to Achieve?#

When communication service providers were asked about the experience of these new technological tools for network sites, they felt it was better than on-site conversations. They reveal that these online calls help everyone look at the same thing and avoid confusion, which was the biggest problem in on-site meetings. Therefore, this reduces the queries, and teams could complete the deal in less time than offline sales.

The most significant benefit is for the technical product managers. They can now work on online techniques for vertical inspection of assets and sites. In addition, 3D modeling is enhanced, and the ground-level captured images ensure efficiency.

Rounding Up About 5G Networks Innovation:#

The year 2020 was indeed a gloomy year for many of us. But the only silver lining was the announcements of technological advancements like the 5G launch, even during these unprecedented times. The technology advancement enabled us to use this pandemic wisely, and we deployed the 5G network at several places. So, we can say that the innovations remained intact even during the pandemic because of intelligent and relevant technologies. Therefore, it would not be wrong to conclude that technological advancements have won over these challenging times and proved the future.

AI (Artificial Intelligence) and ML (Machine Learning) are all set to become the future of technology. According to reports, AI and ML will become crucial for intelligent edge management.

Summary#

We can't imagine Intelligent Edge computing without AI and ML. If you are unaware of the enormous impact of AI and ML on Intelligent edge management, this article will help you uncover all the aspects. It will tell you how AI and ML will become the new normal for Intelligent Edge Management.

What is Intelligent Edge Computing?#

Edge Cloud computing refers to a process through which the gap between computing and network vanishes. We can provide computing at different network locations through storage and compute resources. Examples of edge computing include “on-premises at an enterprise or customer network site” or local operators like Telco.

Predictions of Edge computing:

We expect the future of edge computing to grow at a spectacular rate. Since edge computing is the foundation of the network computer fabric, experts predict a steady growth of the popularity of edge computing shortly. Adding to these predictions are the new applications like IoT, 5G, smart devices, extended reality and Industry 4.0 that will enable rapid growth of edge computing. According to a prediction by Ericsson, by 2023, almost 25% of 5G users will start using intelligent edge computing. These predictions reflect the expected growth of edge computing shortly.

Challenges with Edge computing

Every coin has two sides. Similarly, if edge computing is expected to grow substantially, it will not come without common problems and challenges. The first problem is the gap between existing cloud management solutions and computing at the edge. The cloud management solutions that exist today work on large pools of homogeneous hardware, making it difficult to manage. Besides that, it requires 24/7 system administration. But if you look at the suitable environment for edge computing, you would see significant differences.

• It has limited and constrained resources:

Unlike the existing cloud management solutions, edge computing is limited by constrained resources. This is because the location and servers are made with a small factor of rack space in mind. This might seem like an advantage because you will require less space, money, etc. But the challenge with this is that one needs to have optimum utilisation of resources to get efficient computing and storing facilities.

• Heterogeneous hardware and dynamic factors:

The other significant difference is that, unlike the existing resources that require homogeneous hardware, edge computing requires diverse hardware. Therefore, the requirement can vary at different times. Requirements for hardware can vary according to varying factors like space, timing, the purpose of use etc. Let's look at some of the diverse factors that influence the heterogeneity and dynamics of edge computing:

• Location: If edge computing is for a commercial area, it will get overburdened during rush hours. But in contrast, if you are using it in residential areas, the load will be after working hours because people will use it after coming home. So in this way, the location can matter a lot for edge computing.

• Timing: There are several hours in the day when edge computing is widely used, while at some hour's its application is negligible.

• Purpose of application: The goal of computing is to determine what kind of hardware we require for edge computing. If, for IoT, the application will need the best services. But if it is for a simple purpose like gaming, even low latency computing would work.

• In this way, we see that edge computing has to overcome heterogeneity and diversity for optimum performance.
• Requirement of reliability and high performance from edge computing:

The third challenge for edge computing is to remain reliable and offer high performance. There is a dire need to reduce the chances of failure that are most common in software infrastructure. Therefore, to mitigate these failures, we need timely detection and analysis and remedy for the problem. If it is not correct, it can even transfer from one system to another.

• The problem of human intervention with remote computing:

If edge servers are in a remote area, there will be a problem with human intervention. Administrators can't visit these remote areas regularly and check on the issues. Therefore, there is a need for the part of computing to become self-managing.

How AI and ML are expected to become of utmost importance for edge computing?

Artificial intelligence and machine learning are expected to become crucial for computing because the distribution of computer capability and the network has several challenges in operation. Hence AI and ML can overcome these challenges. AI and ML will simplify cloud edge operations and ensure a smooth transition of edge computing.

• AI and ML can extract knowledge from large chunks of data.
• Decisions, predictions, and inferences reached through AI and ML are more accurate and faster at the edge.
• By detecting data patterns through AI and ML, Edge computing can have automated operations.
• Classification and clustering of data can help in the detection of faults and efficient working of algorithms.

How to use AI and ML for edge computing?#

Enterprises can use AI and ML in different mechanisms at edge computing locations.

Let's look at the different tools and processes involved.

• Transfer learning (new model training from previously trained models)
• Distributed learning
• Federated learning
• Reinforcement learning
• Data monitoring and management
• Intelligent operations.

Conclusion#

We can expect extended artificial intelligence and machine learning on edge to become a new normal. It will affect almost all technological tools, including edge computing. In this article, we looked at how artificial intelligence and machine learning would help edge computing in the future to overcome its challenges. But it will always remain essential to have a robust framework for technological tools not to be misused.

Why are videos the best to use with Edge? What makes edge special for Videos? This article will cover aspects of Video at Edge why it is a Unilateral choice! Read on!

The Simplest, Smartest, Fastest way for Enterprise to deploy any application

With advancements in computing, we are making newer technologies to improve end-user performance. The tool to help us in getting the best user experiences is edge computing. As cloud computing is gaining momentum, we have created better applications that were impossible earlier. Given the vast arena of edge computing, we can get several benefits from it. Therefore, we should not restrict to only content and look beyond what is available presently.

We all know that our computer applications depend on the cloud for efficient operations. Still, certain drawbacks of this dependence include buffering, loading time, reduced efficiency, irritation, etc. This article will look at how we can get the best video experience at the Edge.

How can the Edge help us in getting the best video experience?#

We all love to watch different genre videos on our smartphones, laptops, PC and other devices. But these are only limited to a restricted view; we don't feel them in reality. Therefore, several tech projects look into the possibility of creating a 360-degree video experience. Several tools like head-mounted Display (HMD), also called virtual reality, can come in handy for this 360-degree video viewing. It creates more interest and unique ways than a traditional video viewing experience. However, there are several challenges that this technology has to overcome to provide a better user experience.

Challenges for better user experience for videos at edge#

• High Bandwidth is required to run these immersive videos
• Latency sensitivity is another problem.
• Requirement of Heterogeneous HMD devices for getting a 360-degree experience

However, edge computing can help us overcome these challenges and enhance the user experience.

What is edge computing?#

The Edge often termed the next-gen solution, can help us get the best video experience because it allows us to view unlimited content on different devices. The quality is improved because the content is stored near the end-user. Interestingly, Edge can help get an enjoyable experience with no regard to the location.

Take videos loading, for example, it is faster in edge computing rather than cloud computing. To check the video experience in edge computing, users played an edge gaming app(a smartphone multiplayer video game). They did the entire process on edge rather than on a mobile phone. This experiment showed spectacular results with remarkable speed.

In a video operating system that gets help from the Edge, the viewers get a 360-degree viewpoint on edge servers. The algorithms involved in Edge can help implement and solve the problems of video streaming systems.

Benefits for using Edge for video streaming

• Edge helps in reducing bandwidth usage. Therefore, there is a reduction in loading and buffering issues.
• The computation workload on HMD (Head Mounted Display) is reduced because lightweight models are used.
• The users could realise lower network latency.
• Let's compare it with traditional video streaming platforms. We will get 62% better performance because it reduces bandwidth consumption by almost sixty-two per cent and renders the highest video quality to the viewer.
• The battery life is also enhanced because the Edge consumes far less battery than traditional video streaming platforms.

Imagine the possibility of hosting a whole application on edge computing

We have seen how edge computing can offer wonderful video experiences. Let's see how edge computing can help us in hosting a whole application and getting maximum satisfaction. According to the study, if we upload the entire application at the Edge, we would need o

nly a front-facing client to operate with no other requirement.

An excellent example to understand this concept is Google glass. If we watch an application on Google glass, we can see that it is not hosting the application, but it is only a medium to view it. Similarly, smartphones would not host the application but become a medium to view the application. It could therefore show spectacular performance.

Enhanced Experience is not the only benefit by hosting on Edge

• We will see the first change in the landscape of application.

Edge will make the application more interactive, intelligent and exciting, thus giving a better user experience.

• The application hosted on Edge will not need to depend on a smartphone but only on the network.
• The requirements for allied technology like power, battery, memory for smartphones will reduce since we host the applications on Edge and not on the smartphone.

In addition, it will help smartphone manufacturers to give necessary attention to hardware components like display, screen, etc.

Hosting applications at the Edge will bring a revolution and how we perceive smartphones. We will, then, use smartphones only for viewing the application and not for storing the application.

As the load on smartphones reduces, companies can remove unnecessary technology from smartphones. Users can get slim, thin, foldable (as the latest technology is trying to give) and even unimagined smartphones in the future.

How does an application get to be a part of edge computing?#

We saw how edge computing could help us in getting an excellent video and application experience. But we don't want to give this to theory, only instead bring it to practical use. For making it a reality, there are specific requirements. The first requirement for hosting applications or videos on Edge rather than a smartphone is an edge computing platform. Only an edge computing platform will enable us to get the benefit of network and application. Therefore, several companies like Nife.io are working on creating an ‘OS for the edge'.

Rounding up:

In this article, we saw how edge computing could help render better quality videos, solve the existing problems of video streaming platforms and give the best user experience. But for all of this to be a reality, we require platforms to adopt edge computing.

Therefore, welcome the future of video streaming and reap the benefits of edge devices by reaching out to us. We are soon to realise the benefits of the videos listed above due to edge computing.

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