Exploring Edge Computing: Revolutionizing Data Processing
Innovation is an important staple in any industry, and one industry where innovation is a constant trend and aspiration is technology. From faster computers to better cameras and more durable devices, there are various dimensions in which innovation is pursued and delivered. Often, however, we look at innovation and this sort of progress in terms of new products and software. Innovation takes many other forms, though, including the mechanism, structures, and processes that enable our devices to function. If we’re talking about computing, perhaps just as critical as the power of a computer is how efficiently the data is being processed. One significant development in this field has been the introduction and adoption of edge computing.
The Evolution of Data Processing
To better understand just what edge computing is and how it works, it might be useful to turn back the clock on data processing and computing as a whole. Think back to the big, blocky desktop computers, before connecting to the internet basically automatic, or even super possible. Those computers had no option to compute on a “cloud,” and computing had to be done directly on the machine. For very complicated and taxing computations, institutions often housed much larger computers, but the computation still took place on the same machine.
Fast-forward a few decades, and we arrive in a world where computational capacity is not limited by the processing power of our personal computers. A speedy internet connection allows us to send data not just for the sake of communication, but also for the sake of computation. Your phone or personal computer might not have the computational power to crunch all the data you go through for different tasks. But it can send that data over to another computer that will do the heavy lifting, and send the results right back to you. This is cloud computing, and it revolutionized data processing across the board.
Still, as with many technological problems, there is no one silver bullet when it comes to data processing. Cloud computing led us to drastically lower the barrier of entry to complex and heavy computational tasks, but not every task needs a remote supercomputer to be completed. In fact, in many contexts, sending data over the cloud is actually a less efficient and more costly process than running the data processing on the device itself. This marks the return to in-device computation and, since our devices are now at the “edge” of a larger network, we call this “edge computing.”
Efficiency on a Spectrum
When sending information to the cloud for processing, your data has to be packaged and sent out from your device to wherever it will be processed, received, computed, re-packaged, and sent back your way for your device to unpack once again. Although this often happens in the blink of an eye, it can still be much costlier in terms of time and resources compared to just running the computation on your device. Especially if it’s a quick and simple enough computation to be handled by your limited in-device computational power. Your own device also has the benefit of exclusivity, whereas a central cloud computer might be processing data for several requests at a time, which takes up more resources and might delay your results.
While there are absolutely reasons to delegate some computation to more powerful computational centers, there are often instances in which keeping computation on “edge” devices is more efficient for a variety of reasons. This decentralization is particularly beneficial when lightning-fast computation is required, but there are other benefits to be explored, like security.
In general, as most things go, neither cloud computing nor edge computing are overwhelming winners. Best practices lead us to a spectrum in which sometimes cloud computing is preferred, sometimes edge computing is preferred, sometimes it is a mix of both, and the deciding factor(s) can be so much more than just speed.
Benefits and Drawbacks of Edge Computing
As mentioned, one of the strongest benefits of edge computing is low latency. In contexts like medical equipment and self-driving cars, the bit of time it takes to send and receive information can be the difference between business as usual and disaster. Keeping the computation in-device lets your self-driving car make lightning-fast calculations and decisions that keep you and other drivers alive.
Additionally, there’s the benefit of privacy and security. If your phone’s virtual assistant adapts to better learn your voice, you might not want this data being sent into a central hub for privacy reasons. The same might be true about your driving habits or other personal information. Edge computing allows this data to be collected and analyzed in order to provide the services you need and expect, without necessarily having to give up these personal details to the company providing the product or service.
Just as there are plenty of benefits, naturally, there are also drawbacks to edge computing. One big drawback is that the efficiency and even feasibility of edge computing is entirely dependent on geography and local infrastructure. If you design your app to function “on the edge,” people who live in rural areas or who otherwise might not have easy, reliable access to the internet might not be able to use your product or service. Your app might work amazingly in a major city with great connectivity, but might be virtually unusable to a huge part of the population.
This list of pros and cons can be dragged on as long as we’d like, and it goes to show that, as we mentioned, there is no one-size-fits-all solution to computational processing. Speed and power are only two of many factors at play, and thoughtful development takes these and many more factors into account when deciding how data needs to be processed. Nevertheless, edge computing has given us yet another tool to build the strongest, fastest, most efficient programs and applications we have ever seen, and has been an important development tool to have on your belt for several years now.
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