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!

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.

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.

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.

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.

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

Learn more about Artificial Intelligence - AI in the workforce in this article.

Introduction#

An increase in data usage demands a network effectiveness strategy, with a primary focus on lowering overall costs. The sophistication of networks is expanding all the time. The arrival of 5G on top of existing 2G, 3G, and 4G networks, along with customers' growing demands for a user platform comparable to fibre internet, places immense strain on telecommunication operators handling day-to-day activities (Mishra, 2018). Network operators are also facing significant financial issues as a result of declining revenue per gigabyte and market share, making maximizing the impact on network investment strategies vital for existence.

How can businesses use AI to change the way businesses make network financial decisions?#

From sluggish and labor-intensive to quick, scalable, and adaptable decisions - The traditional manual planning method necessitates a significant investment of both money and time. Months of labor-intensive operations such as data gathering, aggregation of data, prediction, prompting, proportioning, and prioritizing are required for a typical medium-sized system of 10,000 nodes. Each cell is simulated separately using machine learning, depending on its special properties. Several Key performance indicators are used in multivariable modeling approaches to estimate the efficiency per unit separately. By combining diverse planning inputs into the application, operators may examine alternative possibilities due to the significant reduction in turnaround time (Raei, 2017).

Moving from a network-centric to a user-centric approach - Basic guidelines are commonly used to compare usage to bandwidth. Customer bandwidth is influenced by some parameters, including resource consumption, such as DLPRB utilization. Individual unit KPI analysis utilizing machine learning solves this inefficacy, with the major two processes involved being traffic prediction and KPI predictions. The Key performance indicator model is a useful part of cognitive planning that is specific to each cell and is trained every day using the most up-to-date data. The per-cell model's gradient and angles are governed by its unique properties, which are impacted by bandwidth, workload, broadcast strength, as well as other factors (Kibria et al., 2018). This strategy provides more granularity and precision in predicting each cell's KPI and effectiveness.

From one-dimensional to two-dimensional to three-dimensional - Availability and efficiency are frequently studied in a one-dimensional manner, with one-to-one mappings of assets such as PRB to quality and productivity. Nevertheless, additional crucial elements such as broadcast frequency or workload have a significant impact on cell quality and productivity. Optimal TCO necessitates a new method of capacity evaluation that guarantees the correct solution is implemented for each challenge (Pahlavan, 2021).

Candidate selection for improvement - Units with poor wireless reliability and effectiveness are highlighted as candidates for improvement rather than growth using additional parameters such as radio quality (CQI) and spectrum efficiency in cognitive planning. As a first resort, optimization operations can be used to solve low radio-quality cells to increase network capacity and performance. Instead of investing CAPEX in hardware expansion, cognitive planning finds low radio-quality cells where capacity may be enhanced through optimization (Athanasiadou et al., 2019).

Candidate selection for load-balancing#

Before advocating capacity increase, cognitive planning tools will always model load-balancing among co-sector operators. This is done to eliminate any potential for load-balancing-related benefits before investing. The load-balancing impact is modeled using the machine-learning-trained KPI model by assuming traffic shifts from one operator to another and then forecasting the efficiency of all operators even in the same section (He et al., 2016). If the expected performance after the test does not satisfy the defined experience requirements, an extension is suggested; alternatively, the program generates a list of suggested units for load-balancing.

Prioritization's worth for AI in the workforce#

When network operators are hesitant to spend CAPEX, a strong prioritizing technique is vital to maximizing the return on investment (ROI) while guaranteeing that even the most relevant aspects are handled. This goal is jeopardized by outdated approaches, which struggle to determine the appropriate response and have the versatility to gather all important indicators. In the case of network modeling, load corresponds to the number of consumers, utilization (DLPRB utilization) to the space occupancy levels, and quality (CQI) to the size (Maksymyuk, Brych and Masyuk, 2015). The amount of RRC users, which is near to demand as a priority measure, is put into the prioritizing procedure, taking into account the leftover areas. Further priority levels are adjusted based on cell bandwidth, resulting in a more realistic order.

Developers give ideal suggestions and growth flow (e.g. efficiency and load rebalancing ahead of growth) and generate actual value by combining all of these elements, as opposed to the conventional way, which involves a full examination of months of field data:

• Optimization activities are used as a first option wherever possible, resulting in a 25% reduction in carrier and site expansions.

• When compared to crowded cells detected by operators, congested cells found by cognitive planning had a greater user and traffic density, with an average of 21% more RRC users per cell and 19% more data volume per cell. As a result, the return on investment from the capacity increase is maximized (Pahlavan, 2021).

• Three months before the experience objective was missed, >75 percent of the field-verified accuracy in determining which cells to grow when was achieved.

• Reduce churn

Conclusion for AI in the workforce#

The radio access network (RAN) is a major component of a customer service provider's (CSP) entire mobile phone network infrastructural development, contributing to around 20% of a cellular manufacturer's capital expenditures (CapEx). According to the findings, carriers with superior connection speeds have greater average revenue per user (+31%) and lower overall turnover (-27 percent) (Mishra, 2018). As highlighted in this blog, using Machine learning and artificial intelligence for capacity management is critical for making intelligent network financial decisions that optimize total cost of ownership (TCO) while offering the highest return in terms of service quality: a critical pillar for customer service provider's (CSP) commercial viability.

Learn more about Nife to be informed about Edge Computing and its usage in different fields https://docs.nife.io/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!

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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