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What Is Edge Computing?

At the fundamental level, edge computing prefers computation and data storage closer to the devices where it’s being collected, rather than relying on a central location that can be hundreds of miles away. This gets done so that information & data, especially real-time ones, does not suffer latency troubles that can impact an application’s performance. Additionally, companies can save tons of money by having the processing done locally, lessening the amount of data that requires to be processed in a centralized or cloud-based location. Generally, Edge Computing was devised owing to the exponential growth of IoT devices, which connect to the internet for either gaining information from the cloud or providing data back to the cloud. Along with that, many IoT devices create enormous amounts of data during the course of their operations.

Why Does Edge Computing Matter?

For many organizations, the cost savings can be the only driver towards employing an edge-computing infrastructure. Companies that enfold the cloud for many of their applications may have realized that the costs in bandwidth were higher than they ever expected.

Progressively, enormous advantages of edge computing is an ability to process and store data faster, enabling for more effective real-time applications that are much more reproving to companies. Before the advent of edge computing, a smartphone scanning a person’s face for facial recognition would require to run the facial recognition algorithm via a cloud-based service, which would consume a lot of time to process. With an edge computing model, the algorithm could process locally on an edge server or gateway, or even on the smartphone itself if required, given the enhancing power of smartphones. Applications such as virtual and augmented reality, self-driving cars, smart cities and even building-automation systems need swift processing and response.

It’s crystal clear that while the prime goal for edge computing was to lessen bandwidth costs for IoT devices over long distances, the magnification of real-time applications that need local processing and storage capabilities will drive the technology forward over the upcoming years.

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