Edge Computing is a simple concept. It’s the idea of pushing “intelligence” to an edge of the network, which will reduce the load on core servers and the network itself and make it more efficient overall.
Edge Computing, like many technological advances, was born out of a need. What is the problem? The problem?
You may recall the security camera example if you have read The Hottest IT Trends Of Our Time. This is a scenario where you have several security cameras recording and sending information to a central server.
If you only have a few cameras, this is not an issue. However, if you are in a situation like London’s, with over 400,000 cameras, this can cause a major problem: overloading the main server and network, which can and likely will break everything, even if you have a large pipe.
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In a case similar to London’s, you would want to filter the information being sent to the network’s main servers. As you may already know, this would require data-generating devices that can make decisions and identify relevant information.
Edge Computing can be used for many other purposes than surveillance cameras. Surveillance cameras are the most popular use case for Edge Computing, as they consume a lot bandwidth at peak operation. There are many things you can do with surveillance camera data, something that was impossible in the past few years, when most cameras were still “dumb”.
You could, for example, be a British government agency looking for a London criminal. You scan the face of the criminal to create a biometric map. You can then set up your security cameras to report back to the main servers only if they detect someone matching that biometric map. (Obviously, this would require facial recognition software).
This is possible only if the security cameras at the edge of the network can make decisions. The network’s main servers would crash processing the data from the over 400,000 cameras streaming 24/7.
Although the overload problem was not an easy one, network engineers can now integrate sensors and software into “edge” devices. These devices are devices that live at the edge of the network. This allows them to configure the devices to only send the relevant information to the main servers. In other words, they can make decisions without relying on any other than their own computing power. This makes things easier for everyone.
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Edge Computing can reduce network load, increase efficiency, functionality of devices, and speed up information processing because data doesn’t need to travel far to be processed. Edge Computing is not without its problems. There are also smaller issues that can impact business operations. Let’s take a look on the pros and cons of Edge Computing.
Pros to Edge Computing
Network load is reduced
Let’s return to the London security camera case for a moment. Imagine that you are the security officer for the entire city and have decided to upgrade your cameras to stream in 4k resolution to make better use.
A 4k definition stream takes around 25Mbps. If all these cameras could only send information back, then your network would need to process the information from more than 400,000 cameras every second. My friend, in that scenario, you would have to wait for technology’s ability to meet your needs.
These are all possible with Edge Computing. Edge Computing allows networks to scale to meet IoT demands without worrying about overconsumption or waste of resources processing irrelevant data.
Edge Computing devices can be programmed by network engineers to perform many different functions. Although I have already covered filtering data before it is sent over the network, Edge Computing devices can do many other things. Because they have their own software, and can process their data, they can be configured in ways to handle edge data that are not yet possible.
Networks will undoubtedly become more functional and more efficient thanks to the new capabilities offered by leveraging data at their edges.
Efficiency (real-time access to analytics)
Edge Computing also offers businesses the ability to perform real-time data analysis on-the-spot, which is a huge advantage.
If you are part of a manufacturing company with multiple manufacturing plants, you might be able to analyze your plant’s data while it is being recorded. Instead of waiting for the data to be sent to a central server, you can do this immediately and have your data returned to your plant.
This speed results in immediate action which ultimately leads to cost reductions or revenue increases (the main thing businesses strive to do).
Imagine yourself as a manager at a manufacturing plant and having an issue with your production process. You have to wait for the company’s main servers to analyze your data. Edge Computing allows you to find the problem almost immediately. Thus,