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Defining the Edge Part 2: 4 Use Cases for Edge Computing

Posted by Enconnex Team on October 30, 2020

| Categories: Edge Computing

In our last post Defining the Edge Part 1, we discussed the edge computing concept and four key characteristics of edge data centers. More and more organizations are looking to increase efficiency, relieve network congestion, and reduce costs by putting IT resources closer to users and devices. These resources are housed in smaller, remote data centers capable of delivering high-performance, highly reliable services, with streamlined operations using remote management tools.

The IT industry has moved back and forth between centralized and decentralized models. The mainframe era gave way to client-server computing, then shifted back to the centralized approach with the rise of the cloud. Now, edge computing is decentralizing applications and data away from the corporate data center and the cloud. But edge computing is more than just another swing of the pendulum. Rather than replacing the cloud, it complements it, providing unique capabilities for specific use cases.

 

4 Use Cases for Edge Computing

 

1. Internet of Things (IoT) Applications

This is probably the biggest driver of edge computing adoption. The value of the IoT lies in its ability to support real-time asset monitoring, intelligent automation, and operational intelligence. Edge data centers provide the resources needed to process and analyze IoT data near the source for faster decision-making. According to Gartner, approximately 75 percent of data will require real-time analysis and action at the edge by 2022.

2. Virtual Reality (VR)

Just about any physical process can be replicated in VR. For example, VR can be used in manufacturing to test parts and processes, saving companies millions by eliminating the need for full-scale prototypes. Retailers can use smart mirrors to enable customers to “try on” clothes without a dressing room. Creating a virtual environment that’s realistic and allows users to interact with it naturally requires substantial processing power and high-speed data transmission. Edge data centers can provide those resources.

3. Artificial Intelligence

AI applications are often implemented in a hybrid approach. The AI models are developed and trained in a centralized data center or the cloud using historical data, then pushed to the edge to inference current data. Inferencing requires less computing power than training but needs lower latency. Edge AI has applications in many industry sectors — in manufacturing, for example, AI can analyze data collected from sensors to improve quality, reduce waste, and drive down costs.

4. Regulatory Compliance

Government and industry regulations are increasingly concerned with data sovereignty — data must be maintained in a geographic location near the user in order to meet privacy requirements. Storing and managing data in an edge data center can facilitate regulatory compliance while enabling more efficient delivery of services to users. For example, retailers can collect and analyze data from the Wi-Fi network and provide personalized content without running afoul of privacy laws.

 

Enconnex Is Your Source for Edge Solutions

Edge data centers must be carefully designed and architected in order to deliver the performance, bandwidth, and security needed to enable these use cases. It’s not just a matter of IT equipment — you need a solid foundation of racks, cabinets, cooling units, and other data center infrastructure. Remote management is also a must. Enconnex has the expertise and experience to help you develop and implement a successful edge strategy. 


Posted by Enconnex Team on October 30, 2020

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