The Significance of 5G and Edge Analytics towards Real-time and Intelligent Enterprises – Jio

This article covers edge computing, edge-native applications, edge infrastructure clouds, edge platforms, edge analytics, edge artificial intelligence, cloud-native edge computing, and serverless edge computing, in addition to 5G and Edge Computing combination.
The Significance of 5G and Edge Analytics - Jio - TeckNexus

The Solidity of Edge Computing

Undoubtedly with the faster proliferation of multifaceted IoT sensors and devices in and around us, the prospects for the newer phenomenon of edge computing brightens significantly. Resource-constrained IoT devices are termed as edge devices whereas resource-intensive IoT devices are being touted as edge servers. Such a distinction is necessary for gaining a deeper understanding of IoT systems and environments. The industry has been fiddling with cloud computing for a long. Now with IoT edge devices are being stuffed with more memory and storage capacities and extra processing capabilities, the real edge era is, to begin with, all the clarity and confidence. Powerful processor architectures are emerging to artistically empower IoT devices to be productive, participative, and cognitive. IoT devices are increasingly self-, surroundings, and situation-aware yet they are slim and sleek, handy and trendy. Through a bevy of advancements, edge devices in our everyday environments (homes, hospitals, retail stores, manufacturing floors, eating joints, nuclear establishments, entertainment plazas, railway stations, ports, etc.) are adroitly strengthened to join in mainstream computing thereby the idea of edge computing has started to flourish with correct nourishment. 

Edge-native Applications

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 corroborations. 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. In the subsequent sections, we illustrate edge-native applications. 

Edge Infrastructure Clouds

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. 

Edge Platforms

The above-mentioned 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. Further on, 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. 

Edge Analytics

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

This 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. 

Cloud-native Edge Computing

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.  Automated updates provide risk-free and secure software. Traditional embedded applications are OS-dependent, which makes migrating and scaling applications across new infrastructures complex and risky. There are lightweight Kubernetes implementations such as KubeEdge (https://kubeedge.io/en/), K3s (https://k3s.io/), and MicroK8s (https://microk8s.io/) for enabling edge device clouds. 

Serverless Edge Computing

Serverless eliminates infrastructure maintenance tasks and shifting operational responsibilities to a cloud or edge vendor. Serverless has been popular in the traditional cloud world. Now with Kubernetes, containers, MSA, and event-driven architecture (EDA) at the edge, the pioneering serverless principles are getting applied in order 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. 

The Combination of 5G and Edge Computing

For ultra-low latency and ultra-high reliability, 5G communication comes to the rescue. On the other side, towards the last-mile connectivity, edge clouds help 5G communication. Their association surmounts the limitations of each other so that the real digital transformation empowers business and people transformation. 

Conclusion

The fusion of edge computing and 5G communication is to result in a series of 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. 

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