5G & Edge

Recent Updates

TERAGO has established Canada’s premier 5G MMwave private network for Industry 4.0 research at McMaster University’s new Manufacturing Research Institute (MMRI) in Hamilton, Ontario. Over the next three years, this network will enable researchers to test and develop advanced manufacturing technologies utilizing 5G MMwave capabilities.
According to ABI Research, 5G fixed-wireless access (FWA) services are increasingly being seen as a competitive alternative to traditional fixed broadband in both developed and emerging markets. The firm predicts that the number of 5G subscriptions of FWA could reach 72 million by 2027, which would represent 35% of the total fixed-wireless market.

Updates from External Media



Edge computing is a concept that piggybacks off the ideas of cloud computing but differs in how it handles heavy processing. For example, off-loading tasks that would be too much for the central network to handle to local devices close to where they are being used is what edge computing does.

It involves processing data at or near the point it is created before transmitting the data to servers in the cloud. This could be anything from transferring information being communicated by drones, moving content to various points around an exhibition hall, and real-time video analytics in sports stadiums.

The processing of data near the point where it is created helps make the internet more efficient by reducing the need for data transmission back and forth between devices. Edge computing also reduces latency by speeding up how quickly things are processed, which is useful for services like self-driving cars that require quick responses.

For ultra-low latency and ultra-high reliability, 5G network comes to the rescue. On the other side, towards the last-mile connectivity, edge clouds help 5G networks. Their association surmounts the limitations of each other so that the real digital transformation empowers business and people transformation. Applications are increasingly going to process more data and make decisions closer to the edges for better real-time user experience, compliance, etc. Gartner estimates that by 2025, 75% of enterprise-generated data will be outside of a central data center or cloud.

Use cases across verticals need geo-distributed application architectures – autonomous vehicles, digital healthcare, smart retail, smart cities, and industrial automation are examples. 5G is an important enabler for edge computing. In addition, a confluence of factors are coming together that will enable Edge (geo-distributed) computing.

  • Network Speeds – 5G offers 10x-20x higher speeds (multi-Gbps) and lower latencies when compared to 4G with improved reliability. This enables a better experience for distributed applications.
  • Compute Costs – Specialized hardware such as GPUs, TPUs have now become more affordable to be used at the edge. So one could envision offloading a compute-intensive task such as ML closer to where data sources are.
  • Distributed Cloud and Data Center footprint – The large cloud providers themselves are expanding their footprint. In addition, companies such as Equinix Metal, Vapor IO, Cox Edge are building micro data centers and services.
  • Modern applications that lend well to distribution – Many enterprises have focused on adopting microservices-based application architecture, where components are loosely coupled but tightly connected. This framework will help in distributing applications.

The fusion of edge computing and the 5G network will continue to drive versatile and resilient applications for businesses and commoners. The unique blend is to open up fresh possibilities and opportunities. There are product and platform providers and start-ups in plenty exquisitely leveraging their combined capabilities to do the justice for the ensuing digital era. There will be cool interactions between enterprise, embedded and cloud systems to visualize and realize cognitive applications. The transition from business IT to people IT gets exemplified through this harmonious linkage.

With the projected billions of IoT devices and trillions of IoT sensors is tending towards reality, there is a possibility for producing a massive amount of multi-structured IoT data through their voluntary collaborations, correlations, and corroboration. With such exponential growth in the IoT data size, traditional cloud processing is to face a number of hitches and hurdles. The way forward is to filter out repetitive and routine data at the source itself. Such an arrangement goes a long way in saving precious network bandwidth.

Securing data in transit and rest is another challenge widely associated with conventional and classical computing. The security of IoT data is ensured as there is no need to traverse every bit of data to faraway cloud storage and analytics over the porous, open, and public Internet, which is the prominent and affordable communication infrastructure for the IoT era.

The network bandwidth gets preserved due to edge computing. Above all, the real-time data capture, storage, processing, analytics, knowledge discovery and dissemination, and actuation get fully accomplished through edge computing. What edge computing brings to the table is the local storage and proximate processing of edge data. Such a paradigm shift brings in a dazzling array of sophisticated edge applications.

The quantity and quality of edge applications are the key motivation for the huge success of edge computing. The realization of elusive context-aware applications is spruced and speeded up with the widespread IoT device deployments in crowded and mission-critical environments. Multi-device applications, which are typically process-aware and business-critical, are bound to see the light. There are numerous business, technical and user cases getting articulated and accentuated through the unprecedented adoption of edge computing.

While stability, scalability, and security are the key pillars of 5G+ edge business applications, stability may be addressed through efficient DevOps, reliable infrastructures, and high availability systems. Scalability may be addressed by over-sizing the resources or by having spare clusters on hot stand-by. Security on the other hand cannot be substituted by alternate approaches. With multiple application layers, distributed microservices, hardware-software integrations, and processing of sensitive data outside of the IT centers, edge applications open up multiple points of “security vulnerability”.

The following list highlights the key aspects to be considered in securing 5G+ edge applications:

  • IoT sensors must be protected physically and digitally
  • The network, 5G+ wireless must be secured for access, interruptions, and attacks

For the complete list with info on “Security vulnerability points in 5G+ edge application” and info on “security threats and risk levels across the edge application layers”, read the 5G Magazine, 5G and Edge edition, article “Securing 5G+ Edge Application” by Prasad Rajamohan.

With the advent of R16 standards, work on the other pillars of 5G – URLLC & massive IoT are underway. To take full advantage of new mobile communications standards, mobile operators are moving from an evolved packet core (EPC) to 5GC. By combining 5G NR deployment in standalone (SA) mode with a virtualized network architecture, operators can efficiently use network slicing to allocate resources for different use cases. As a result, mobile operators need to manage a massive number of IoT devices and connections in support of service level agreements (SLAs) for both consumers and the industry.

MEC – Multi-access Edge Computing is a central piece in moving these areas forward. MEC delivers cloud computing capabilities and an IT service environment at the edge of the cellular network. It allows compute resources to be deployed closer to the edge of the network, in effect “transporting” cloud capabilities to the edge to overcome latency and network reliability issues.

MEC offloads client-compute demand given the higher available communication bandwidth from the client. It allows for lower demand on the network for predictable compute demands by placing them closer to the client. It can also be used to optimize the network function itself because of its large distributed compute capability. MEC also allows for greater flexibility in applications. For example, applications can be architected to run in the client, on edge, in the cloud, or split across multiple domains to optimize performance, power consumption, and other aspects.

For more details, read 5G Magazine, 5G and Edge edition, Keysight article by Kalyan Sundhar, Vice President & General Manager for 5G Core to Edge at Keysight Technologies

MEC provides some unique challenges in terms of testing:

  • Ensuring the right amount of hardware and software services are available to do the required tasks.
  • Slices are instantiated & operate correctly to meet the slated SLAs – Slice assurance.
  • How secure is the Edge? Given the MEC is a minimized data center co-hosting applications, vulnerabilities increase thereby requiring constant tests against attacks & ensuring a strong perimeter defense.

For additional challenges, read 5G Magazine, 5G and Edge edition, Keysight article by Kalyan Sundhar, Vice President & General Manager for 5G Core to Edge at Keysight Technologies.

If we sum all the cloud servers of all the public cloud environments across the globe, the number of commodity cloud servers should be hovering around a few million. But it is projected that there will be 50 billion connected devices across the world in the years to come.

Thus, the computational capability of edge devices is far more superior than all the cloud servers added together. IoT edge devices gain the strength to find and interact with other edge devices in the vicinity to form ad hoc, temporary, dynamic, and purpose-specific clusters/clouds. Edge devices are increasingly integrated with cloud-based software applications (called cyber applications) to gain extra capability.

Digital twins are also being formed and run in cloud environments for complex edge devices at the ground. Thus, edge integration, orchestration, and empowerment are being activated through a host of technological paradigms. Holistically speaking, edge clouds are formed to tackle bigger and better problems. Feature-rich applications can be availed through edge cloud formation. However, there are constraints and challenges in constructing edge device clouds.

Devices are heterogeneous and large in number. As widely accepted, the aspects of multiplicity and heterogeneity lead to unfathomable complexity. Thereby there is a clarion call for complexity-mitigation techniques and tools.

For more details, read 5G Magazine, 5G and Edge edition, “The Significance of 5G and Edge Analytics – towards Real-time and Intelligent Enterprises” article by Pethuru Raj Ph.D.,Vice President at Reliance Jio Platforms Ltd.

The infrastructure complexity is being smartly delegated to competent platform solutions. We have container runtimes for fulfilling software portability, the state-of-the-art hybrid version of microservices architecture and event-driven architecture, DevOps toolkits for frequent and speedy software deployments, and container orchestration platforms such as Kubernetes.

There are shrunken versions of Kubernetes to be deployed in edge servers. Kubernetes is being positioned as the one-stop IT platform solution for forming edge device clouds. Devices expose their unique services to the outside world through service APIs. Messaging middleware solutions are enabling automated and event-driven device interactions.

The large-scale adoption of Kubernetes is accelerating and sustenance of edge device clouds across industrial, commercial, and official environments. Process industries get immense benefits out of containerized and Kubernetes-managed edge clouds. Industry 4.0 applications are being facilitated through the power of Kubernetes. Edge-native applications are built from the ground up with Edge in mind. Edge-native applications intrinsically take advantage of the unique capabilities and characteristics of the Edge. Kubernetes plays a very vital role in shaping up edge-native apps.

The cloud-native paradigm has brought in a few deeper and decisive automation in software engineering including software deployment. Cloud-native applications are being designed, developed, delivered, and deployed across cloud environments (private, public, and hybrid). Highly modular and modern applications are being derived by applying cloud-native principles. Further on, cloud-native applications are highly available, scalable, extensible, and reliable.

Especially the non-functional requirements of applications are fulfilled through the unique cloud-native competencies. Now, this successful computing paradigm is being tried in building and releasing event-driven, service-oriented, and people-centric edge applications. All the complexities of edge-native application engineering are being decimated through the skillful application of the proven cloud-native model. In short, edge computing is getting simplified through the power of cloud-native computing.

Cloud-native applications are to maximize resilience through predictable behaviors. A cloud-native microservices architecture allows for automated scalability that eliminates downtime due to error and rapid recovery in the event of application or infrastructure failure.

Serverless eliminates infrastructure maintenance tasks and shifting operational responsibilities to a cloud or edge vendor. As a result, Serverless has been popular in the traditional cloud world. With Kubernetes, containers, MSA, and event-driven architecture (EDA) at the edge, the pioneering serverless principles are being applied to bring in the same benefits at the edge.

Serverless in a centralized cloud suffers from slow cold starts, and there are a few drawbacks. The edge cloud is being presented as the ideal solution to remedy all of these challenges. A serverless environment that is embedded within an edge cloud provides scale and reliability. Serverless edge truly fulfills a distributed compute power and data processing where it is created.

With path-breaking edge platforms and infrastructures in place, the idea of edge data analytics picks up fast. The noteworthy factor is that edge analytics fulfills the long-standing goal of producing real-time insights. Real-time intelligence is mandated to build next-generation real-time services and applications, which, in turn, contributes to the elegant establishment of real-time intelligent enterprises.

There are fast and streaming data analytics platforms specifically prepared getting deployed in edge servers, which cleanse and crunch edge device data to emit out actionable insights in time. Edge devices generally collect and transmit edge data.

There are sensors, CCTV cameras, robots, drones, consumer electronics, information appliances, medical instruments, defense equipment, etc. in various physical environments to minutely monitoring, measuring and management of physical, informational, commercial, temporal, and spatial requirements proactively and unobtrusively.

The growing number of IoT devices produces ad streams a lot of data at high speed. Low-latency applications such as video surveillance, augmented reality and autonomous vehicles demand a real-time analysis to discover and disseminate real-time knowledge. There are research contributions such as real-time video stream analytics using edge-enhanced clouds.

Edge AI is gaining immense traction these days. There are AI-specific processing elements (graphical processing unit (GPU), tensor processing unit (TPU), and vision processing unit (VPU)) to accelerate and augment data processing at the edge. There are concerted efforts by many researchers to fructify tiny machine learning (ML) processing at the edge. There are several lightweight frameworks and libraries to smoothly run machine learning and deep learning algorithms at the edge.

Classification, regression, clustering, association, prediction requirements are being handled at the edge. AI processing at the edge is the key differentiator for ushering smarter applications such as anomaly/outlier detection, computer vision/facial and face recognition, natural language processing (NLP) / speech recognition, image segmentation, etc. With all-around advancements in the fields of AI and edge computing, edge AI is turning out to be an inspiring domain of study and implementation.

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