As Cloud Computing gradually approaches its optimal potential, a new technology was required that could complement the capability of the cloud. The answer to this need has come in the form of Edge Computing, which is a new but rapidly developing technology.
What is Edge Computing?
Edge Computing can be defined as an elaborate network of inter connected devices that are constantly generating, transmitting, sharing and processing data in very close proximity to its origin. The whole idea behind edge computing is localized processing of data.
How Does Edge Computing Work?
Consider the edge as a highly connected web of intelligent devices that have the capability to generate and process data at a very rapid pace. As global communication channels get more crowded, edge computing was a need of the hour.
However, we need to understand that for edge computing to be fully effective, we need a super fast localized network that can handle such massive rates of data transmission among millions of inter connected smart devices.
Just to give some perspective, the existing cellular and communication infrastructure is considered inadequate for edge computing to display its full potential. For this reason, telecom companies across the globe are gearing up for upgrading their hardware.
This is by no means a small exercise as it involves investing billions of dollars in upgrading the communications infrastructure. Edge computing is aimed at delivering data processing capability at the most localized level possible, bringing a lot of efficiencies.
Why do we need Edge Computing?
Firstly, the existing information and communications infrastructure is based on a data centralization model. This in turn results in serious challenges for transmitting vast amounts of data to centralized locations such as servers or data centers.
In theory, Edge Computing is designed to change the entire data generation, processing and transmission mechanism by making this entire process much more decentralized, as opposed to the existing model of data centralization.
This in turn will reduce the amount of residual data as much of it becomes redundant once the objective for collecting it has been effectively achieved. We can also expect the global flow of data to be channelized more at the local level.