10 posts tagged with "network edge"

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!

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.

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.

Presently, the success of a company entails engaging in digitalization to penetrate market opportunities, connect with consumers in unusual ways, and discover different methods and practices. This entails reversing the conventional corporate model—moving from one that would be compartmentalized and rigid to one that is interconnected and fluid.

Owing to enhanced digital ecosystems which thus offer all-new levels of economic development and return on investment, new types of digital business dialogue and integration (open interconnection) are now conceivable. Because, in the digital era, big players have the finest virtual connectivity, wherein they collect and administer the broadest ecosystem of brand and product suppliers [(Park, Chung and Shin, 2018)]. Digital Ecosystem Management (DEM) is a new business field that has arisen in reaction to digitalization and digital ecosystem connectivity.

Significance of digital ecosystems#

Networking impacts are introduced by [digital ecosystems]. Businesses that integrate with virtualization can create configurable business strategies comprised of adaptable programs and services that can be readily changed out when market demands and/or new technologies dictate [(Hoch and Brad, 2020)]. Implementation of change (like the worldwide COVID-19 epidemic) isn't any more the same as plotting a new path on a cruise liner. Businesses may now react instantly, more accurately, and at a cheaper price than it has ever been.

However, like with any radical transformation, appropriate execution is critical to gaining a competitive edge. Businesses must first select how they want to engage in any particular ecosystem. Instigators define the ecosystem's settings and optimize its worth. Contributors offer assistance through a wide range of commercial formats (service, channel, etc.) and create secondary interconnections. Irrespective of the purpose, each organization must understand its fundamental capabilities and enable other ecosystem participants to produce higher value than would be achievable all alone at mass.

A triad of digital ecosystems#

Every ecosystem contains a variety of people who play distinct yet interrelated and interdependent functions. Presently, there are three fundamental forms of digital ecosystems:

Platform ecosystem#

Businesses that manufacture and sell equipment comprise a platform ecosystem. Networking, memory, and computing are examples of digital fundamental building blocks, as are digital solutions and/or products.

Collaboration ecosystem#

A collaborative ecosystem is a group of businesses that focus on data, AI, machine learning, and the exchange of knowledge to create new businesses or solve complicated challenges [(Keselman et al., 2019)].

Services ecosystem#

A services ecosystem is one in which businesses supply certain business operations and make those activities accessible to other businesses as a service. This enables businesses to build new involved in supply chain models, improving their particular company's operations.

Emerging Digital ecosystem models#

The three unique digital ecosystems spanning multiple sectors include different marketplaces. Businesses from many sectors team up to engage in professional contact events, resulting in the formation of new ecosystem models. Independent retail, economic service, transportation, and logistics ecosystems, for example, are collaborating to establish a new digital ecosystem to generate more effective, value-added distribution networks [(Morgan-Thomas, Dessart and Veloutsou, 2020)].

Best practices in the digital ecosystem#

Businesses must stay adaptable when developing an integrated digital ecosystem. The goal of digital transformation is to remodel an organization's goods, processes, and strengths utilizing contemporary technology [(Gasser, 2015)]. This rethinking cannot take place unless the organization is ready to accept all of the prospective changes. Effective digital ecosystems have the following best practices:

• The business model is being rethought.
• Promoting an open, collaborative culture.
• Bringing together a varied group of partners.
• Create a large user base.
• Make a significant worldwide impact.
• Maintain your technological knowledge.

Gravity and network density of Digital Ecosystem#

Digital ecosystems have a gravitational pull and attract additional members. This increases network connectivity between interconnected ecosystems and data center customers. The removal of the range component eliminates or considerably reduces transmission delay, instability, and errors. Businesses may interface with partner organizations instantly and safely by employing one-to-many software-defined connectivity, such as Equinix FabricTM [(Marzuki and Newell, 2019)].

Interconnectivity changes the dynamics of information and correspondence time. It's the most effective way of getting enormous amounts of data and communication between an expanding number of participants—while maintaining the minimum delay, fastest bandwidth, highest dependability, and fastest connection delivery. And, because all of those linkages are private rather than public, as with the network, the likelihood of cybersecurity threats interrupting any specific ecosystem is much reduced.

Conclusion

Digital ecosystems are a crucial aspect of doing business in the current online market. The breadth of digital ecosystems is fluid, encompassing a wide variety of products, activities, infrastructures, and applications. As a business progresses from the adaptor to attacker, its effect and worth in the digital ecosystem expand from the business level to the ecosystem level. As with any management framework, businesses must change themself in the first phase before reforming their sector and ecosystem in the final phase.

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.

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.

Introduction#

In the field of healthcare technology, we are at the start of a high-tech era. AI technology, cloud-based services, the Internet of Things, and big data have all become popular topics of conversation among healthcare professionals as a way to provide high-quality services to patients while cutting costs. Due to ambitions for global application, the fifth generation of cellular technology, or 5G, has gotten a lot of interest. While the majority of media attention has centered on the promise of "the internet of things," the ramifications of 5G-enabled technologies in health care are yet to be addressed (Zhang and Pickwell-Macpherson, 2019). The adoption of 5G in healthcare is one of the elements that is expected to have a significant impact on patient value. 5G, or fifth-generation wireless communications, would not only provide much more capacity but also be extremely responsive owing to its low latency. 5G opens up a slew of possibilities for healthcare, including remote diagnostics, surgery, real-time surveillance, and extended telemedicine (Thayananthan, 2019). This article examines the influence of 5G technology on healthcare delivery and quality, as well as possible areas of concern with this latest tech.

What is 5G?#

The fifth generation of wireless communication technology is known as 5G. Like the preceding fourth generation, the core focus of 5G is speed. Every successive generation of wireless networks improves on the previous one in terms of speed and capability. 5G networks can deliver data at speeds of up to 10 terabytes per second. Similarly, while older networks generally have a delay of 50 milliseconds, 5G networks have a latency of 1–3 milliseconds. With super-fast connection, ultra-low latency, and extensive coverage, 5G marks yet another step ahead (Carlson, 2020). From 2021 to 2026, the worldwide 5G technology market is predicted to grow at a CAGR of 122.3 percent, reaching $667.90 billion. These distinguishing characteristics of 5G enable the possible change in health care as outlined below.

5G's Importance in Healthcare#

Patient value has been steadily declining, resulting in rising healthcare spending. In addition, there is rising concern over medical resource imbalances, ineffective healthcare management, and uncomfortable medical encounters. To address these issues, technologies such as the Internet of Things (IoT), cloud technology, advanced analytics, and artificial intelligence are being developed to enhance customer care and healthcare efficiency while lowering total healthcare costs (Li, 2019). The healthcare business is likely to see the largest improvements as a result of 5G's large bandwidth, reduced latency, and low-power-low-cost. Healthcare professionals investigated and developed several connected-care use cases, but widespread adoption was hampered by the limits of available telecommunications. High-speed and dependable connections will be critical as healthcare systems migrate to a cloud-native design. High data transfer rates, super-low latency, connection and capacity, bandwidth efficiency, and durability per unit area are some of the distinctive properties of 5G technology that have the potential to assist tackle these difficulties (Soldani et al., 2017). Healthcare stakeholders may reorganize, transition to comprehensive data-driven individualized care, improve medical resource use, provide care delivery convenience, and boost patient value thanks to 5G.

5 ways that 5G will change healthcare#

• Large image files must be sent quickly.
• Expanding the use of telemedicine.
• Improving augmented reality, virtual reality, and spatial computing.
• Remote monitoring that is reliable and real-time.
• Artificial Intelligence

Healthcare systems may enhance the quality of treatment and patient satisfaction, reduce the cost of care, and more by connecting all of these technologies over 5G networks (Att.com, 2017). 5G networks can enable providers to deliver more tailored and preventative treatment, rather than just responding to patients' illnesses, which is why many healthcare workers joined providers during the first round.

Challenges#

As with other advances, many industry professionals are cautious about 5G technology's worldwide acceptance in healthcare, as evidenced by the following significant challenges:

• Concerns about privacy and security - The network providers must adhere to the health - care industry's stringent privacy regulations and maintain end-to-end data protection across mobile, IoT, and connected devices.
• Compatibility of Devices - The current generation of 4G/LTE smartphones and gadgets are incompatible with the upcoming 5G networks. As a result, manufacturers have begun to release 5G-enabled smartphones and other products.
• Coverage and Deployment - The current generation of 4G/LTE smartphones and gadgets are incompatible with the upcoming 5G networks. The present 4G network uses certain frequencies on the radio frequency band, often around 6 GHz; however, such systems are available exclusively in a few nations' metro/urban regions, and telecom carriers must create considerable equipment to overcome this difficulty (Chen et al., 2017).
• Infrastructure - As part of the 5G network needs, healthcare facilities, clinics, and other healthcare providers/organizations will need to upgrade and refresh their infrastructure, apps, technologies, and equipment.

Conclusion#

5G has the potential to revolutionize healthcare as we know it. As we saw during the last epidemic, the healthcare business needs tools that can serve people from all socioeconomic backgrounds. Future improvements and gadgets based on new 5G devices and computers can stimulate healthcare transformation, expand consumer access to high-quality treatment, and help close global healthcare inequities (Thuemmler et al., 2017). For enhanced healthcare results, 5G offers network stability, speed, and scalability for telemedicine, as well as catalyzing broad adoption of cutting-edge technologies like artificial intelligence, data science, augmented reality, and the IoT. Healthcare organizations must develop, test, and deploy apps that make use of 5G's key capabilities, such as ultra-high bandwidth, ultra-reliability, ultra-low latency, and huge machine connections.

References#

• Att.com. (2017). 5 Ways 5G will Transform Healthcare | AT&T Business. [online] Available at: https://www.business.att.com/learn/updates/how-5g-will-transform-the-healthcare-industry.html.
• Carlson, E.K. (2020). What Will 5G Bring? Engineering.
• Chen, M., Yang, J., Hao, Y., Mao, S. and Hwang, K. (2017). A 5G Cognitive System for Healthcare. Big Data and Cognitive Computing, 1(1), p.2.
• Li, D. (2019). 5G and Intelligence Medicine—How the Next Generation of Wireless Technology Will Reconstruct Healthcare? Precision Clinical Medicine, 2(4).
• Soldani, D., Fadini, F., Rasanen, H., Duran, J., Niemela, T., Chandramouli, D., Hoglund, T., Doppler, K., Himanen, T., Laiho, J. and Nanavaty, N. (2017). 5G Mobile Systems for Healthcare. 2017 IEEE 85th Vehicular Technology Conference (VTC Spring).
• Thayananthan, V. (2019). Healthcare Management using ICT and IoT-based 5G. International Journal of Advanced Computer Science and Applications, 10(4).
• Thuemmler, C., Gavras, A. and Roa, L.M. (2017). Impact of 5G on Healthcare. 5G Mobile and Wireless Communications Technology, pp. 593-613.
• Zhang, M. and Pickwell-Macpherson, E. (2019). The future of 5G Technologies in healthcare. 5G Radio Technologies Seminar.

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!

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.