A Guide to Edge Computing

A Guide to Edge Computing

Edge computing is a broad term that refers to a highly distributed computing framework that moves compute and storage resources closer to the exact point they are needed—so they are available at the moment they’re needed. Edge computing companies provide solutions that reduce latency, speeds processing, optimizes bandwidth and introduces entirely different features and capabilities that aren’t possible with data centers. It is a distributed computing paradigm that optimizes Internet devices and web applications by bringing computing closer to the data source or the location where it is required. This reduces the need for long-distance communications between client and server, which means improved response times.

How does Edge Computing Work?

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Edge computing works by capturing and processing information as close to the source of the data or desired event as possible. It relies on sensors, computing devices and machinery to collect data and feed it to edge servers or the cloud. Depending on the desired task and outcome, this data might feed analytics and machine learning systems, deliver automation capabilities or provide insight into the current state of a device, system, or product.

Today, most data calculations take place in the cloud or at a datacenter. However, as organizations migrate to an edge model with IoT devices, there’s a need to deploy edge servers, gateway devices and other gear that reduce the time and distance required for computing tasks—and connect the entire infrastructure. Part of this infrastructure may include smaller edge data centers located in secondary cities or even rural areas, or cloud containers that can easily be moved across clouds and systems, as needed.

Edge computing puts storage and servers where the data is, often requiring little more than a partial rack of gear to operate on the remote LAN to collect and process the data locally. In many cases, the computing gear is deployed in shielded or hardened enclosures to protect the gear from extremes of temperature, moisture and other environmental conditions. Processing often involves normalizing and analyzing the data stream to look for business intelligence, and only the results of the analysis are sent back to the principal data center.

Benefits of Edge Computing

1. Enhanced Speed : From a performance standpoint, edge computing is able to deliver much faster response times. That’s because locating key processing functions closer to end users significantly reduces latency. In traditional networking, data is typically collected on the edge and transmitted back to centralized servers for processing. If a response is needed, these servers then send instructions back to devices on the edge. But with edge computing frameworks, this processing is handled much closer to the source of the data. Devices can respond much faster since they spend less time waiting for data packets to traverse the distance from the edge to the core and then back again.
2. Bandwidth Relief : By keeping more data on the network edge, the overall volume of traffic flowing to and from central servers is reduced. That frees up much needed bandwidth throughout the entire system as a whole, eliminating troublesome bottlenecks and unnecessary processing tasks. End users get the benefit of faster performance since their local network isn’t competing with other regions for limited bandwidth resources.

3. Improved Data Management : Data gathered on the network edge is incredibly valuable because it contains valuable insights into user behavior. Unfortunately, much of that information is also useless “noise,” which is why powerful analytics tools are needed to process that unstructured data to identify meaningful trends. Networks typically transmit all information gathered on the edge back to centralized servers capable of sifting through massive troves of big data.. Rather than transmitting all of that data back to the core, edge networks can process some of it locally and only pass on certain types of information. This frees up valuable processing resources throughout the network and greatly improves the quality of data insights generated by big data applications.
4. Better Security : Although edge computing expands the overall network surface area and increases the number of end points, this doesn’t necessarily mean there are more vulnerabilities to exploit. While it’s obviously important that IoT edge devices are properly secured, the distributed nature of edge networks makes them much more difficult to compromise. If a breach occurs in one area, the compromised portions of the network can be cordoned off without having to shut everything else down. Organizations can also leverage the additional processing resources of the edge network to improve their threat analysis data, which allows them to identify and respond to potential cybersecurity threats much more quickly