Edge Computing is all about bringing processing and storage capability closer to where data is generated. This distributed computing is a great infrastructure strategy where ultra-low latency and real-time response are critical to application performance and user experience.

Faster Response Time: Ingesting and processing data in real-time at the enterprise endpoint improves the application responsiveness securely rather than sending data to and from the central server.

Better Economics: With the exponential rise of data being generated, there is a continuous need for more compute with a faster connecting and more capacity network driving the overall cost. Sending data to the central server only on a need basis and keeping most of the processing at the edge can be more efficient.

 
Autonomous Operations: Downtimes are inevitable; however, system availability can be improved by offloading central computing and connecting networks by bringing workloads closer to the user/data source.

Any use case where faster response time is a requirement or reliable and efficient network bandwidth is a challenge, Edge computing can be a great candidate. A few use cases for example:

Retail: The Edge is going to allow retailers to move forward with every element of their operations and it is going to be the foundation from which they build back post-pandemic. It will enable them to add new applications fit-for-purpose in today’s environment but simultaneously provide a base from which to establish future-proofed practices. Like any foundation, once that is right and secure, it means building on top is much easier on everything from integrating with in-store end points, AI/ML and personalization technologies, fulfilment and shipping and customer intelligence.

Manufacturing and Warehousing: The automation uses ML inference at the customer premises to analyze images and videos to detect quality issues on supply chain and assembly line and initiate actions helping plant engineers address issues fast and eliminate expensive downtime or rework.

Autonomous Vehicle: Cellular Vehicle-to-Everything is a critical platform for enabling autonomous driving and real-time HD maps needed for road safety. Low latency access to the infrastructure that's needed to run data processing and analytics at the edge help with real-time monitoring of data from the vehicle.

Preventive Measures: Edge computing helps diagnose and resolve locally what is not normal resulting in much faster actions. Examples would be remote rigs and manufacturing units.

Law Enforcement: Edge computing is a critical aspect for supporting public infrastructure and law enforcement. For example, A remote location having cameras and analytics capabilities locally so they can identify trespassing/loitering, and only send alarm when those events happen. Footage from squad car dash cams and body cams is uploaded over dual LTE links, where SD-WAN helps improve the connection quality and ensures the traffic sent to a central repository for video post-processing, analysis, and storage.

Healthcare: Diagnostics and imaging take a lot of bandwidth. AI/ML based video analytics and imaging solutions help healthcare professional use speed up the diagnosis of observed conditions. The image or video streams from medical devices are processed at the edge and the response is returned to the user device.

With the continuous rise of IOT or smart devices, the demand to process the data generated is constant. On top workforce, information and infrastructure are not limited to a few enterprises defined locations. This decentralization demands decentralized processing and storage as transporting volume of traffic to and from central systems is as inefficient as it’s expensive. While a single device producing data can transmit it across a network quite easily, problems arise when the number of devices transmitting data at the same time grows.

Another important aspect is driving 5G Connectivity: Since 5G works on low frequency, data needs to travel a relatively more number of hops from user to application. With Edge computing in place, most of the computing can be done at the edge and the rest can be sent to the core if needed.

Edge computing eases strain on the cloud or centralized computing by pushing the processing power to the edge. Edge computing topology has compute and storage resources close to the user or data sources to process, filter, and analyze data and send the results right back to the user in near real time.

An Edge stack, consisting of reasonable compute, storage, and analytic power, is built close to the data source or the user endpoint. This group of edge stacks distributed across the network analyze the data locally and send what needs to be further analyzed  or stored in the cloud or central location, bringing down the turnaround time.

VMware Edge Compute Stack

• Consistent Multi-Cloud Edge Platform: Enterprises can build, run, manage, connect, and protect their industry specific edge-native applications at the Near and Far Edge while leveraging the consistent infrastructure and consistent operations across their Data Centers and Cloud.
• Multi-Cloud Operational Consistency: Simplify operations and management across multi-cloud with consistent, centralized policies for operational consistency.
• Unified and Context-Centric Security: Protect distributed users, data, and applications against threats at all levels. Security is built into the platform for superior protection and is specific to every application and workload.
• AIOps for real-time diagnosis: Measure User and IoT device experience and compare with what’s normal baseline performance is and trigger alerts  to help mitigate issues.
• Optionally enable Private 5G connectivity: SD-WAN together with 5G brings intelligence from the transport layer to enable control using business policies. The transport layer offers status information, allowing unprecedented network self-management and self-healing. It’s perfect for locations requiring fast provisioning of high bandwidth with a high level of control.