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- and ultra-high reliability, 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 and make decisions closer to the edges for better real-time user experience, compliance, etc. Gartner estimates that by 2025, 75% of enterprise-generated will be outside of a central or .
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 . In addition, a confluence of factors are coming together that will enable Edge (geo-distributed) .
The fusion of and the 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 systems to visualize and realize cognitive applications. The transition from business IT to people IT is 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 through their voluntary collaborations, correlations, and corroboration. With such exponential growth in the IoT size, traditional processing is to face a number of hitches and hurdles. The way forward is to filter out repetitive and routine at the source itself. Such an arrangement goes a long way in saving precious bandwidth.
Securing in transit and rest is another challenge widely associated with conventional and classical . The security of IoT is ensured as there is no need to traverse every bit of to faraway storage and over the porous, open, and public Internet, which is the prominent and affordable communication for the IoT era.
The bandwidth gets preserved due to . Above all, the real-time capture, storage, processing, , knowledge discovery and dissemination, and actuation get fully accomplished through . What brings to the table is the local storage and proximate processing of edge . 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 . The realization of elusive context-aware applications is spruced and speeded up with the widespread 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 .
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 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:
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, 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, are moving from an evolved packet core (EPC) to 5GC. By combining 5G NR deployment in standalone (SA) mode with a virtualized architecture, operators can efficiently use slicing to allocate resources for different use cases. As a result, 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 is a central piece in moving these areas forward. MEC delivers capabilities and an IT service environment at the edge of the cellular . It allows compute resources to be deployed closer to the edge of the , in effect “transporting” capabilities to the edge to overcome and reliability issues.
MEC offloads client-compute demand given the higher available communication bandwidth from the client. It allows for lower demand on the for predictable compute demands by placing them closer to the client. It can also be used to optimize the 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 , or split across multiple domains to optimize performance, power consumption, and other aspects.
For more details, read 5G Magazine, 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:
For additional challenges, read 5G Magazine, edition, Keysight article by Kalyan Sundhar, Vice President & General Manager for 5G Core to Edge at Keysight Technologies.
If we sum all the servers of all the environments across the globe, the number of commodity 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 is far more superior than all the servers added together. IoT gain the strength to find and interact with other in the vicinity to form ad hoc, temporary, dynamic, and purpose-specific clusters/clouds. are increasingly integrated with -based software applications (called cyber applications) to gain extra capability.
Digital twins are also being formed and run in environments for complex 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 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, edition, “The Significance of – towards Real-time and Intelligent Enterprises” article by Pethuru Raj Ph.D.,Vice President at Reliance Jio Platforms Ltd.
The 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 -native paradigm has brought in a few deeper and decisive automation in software engineering including software deployment. -native applications are being designed, developed, delivered, and deployed across environments (private, public, and hybrid). Highly modular and modern applications are being derived by applying -native principles. Further on, -native applications are highly available, scalable, extensible, and reliable.
Especially the non-functional requirements of applications are fulfilled through the unique -native competencies. Now, this successful 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 -native model. In short, is getting simplified through the power of -native .
-native applications are to maximize resilience through predictable behaviors. A -native microservices architecture allows for automated scalability that eliminates downtime due to error and rapid recovery in the event of application or failure.
Serverless eliminates maintenance tasks and shifting operational responsibilities to a or edge vendor. As a result, Serverless has been popular in the traditional 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 suffers from slow cold starts, and there are a few drawbacks. The is being presented as the ideal solution to remedy all of these challenges. A serverless environment that is embedded within an provides scale and reliability. Serverless edge truly fulfills a distributed compute power and where it is created.
With path-breaking edge platforms and infrastructures in place, the idea of edge picks up fast. The noteworthy factor is that edge 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 platforms specifically prepared getting deployed in edge servers, which cleanse and crunch edge device to emit out actionable insights in time. generally collect and transmit edge .
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 at high speed. Low- 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 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 at the edge. There are concerted efforts by many researchers to fructify tiny (ML) processing at the edge. There are several lightweight frameworks and libraries to smoothly run 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 AI is turning out to be an inspiring domain of study and implementation.
