Edge computing is a distributed information technology (IT) architecture in which client data is processed as close to the original source as possible at the network’s perimeter.

Edge computing moves certain storage and compute resources out from the central data center and closer to the data source. Instead of sending raw data to a central data center for processing and analysis, this work is done where the data is created, whether that’s in a retail store, a factory floor, a large utility, or throughout a smart city. Only the results of that computer effort at the edge are sent back to the main data center for evaluation and other human interactions, such as real-time business insights, equipment maintenance predictions, or other actionable solutions.

Why has edge computing become so important?

Computing tasks necessitate appropriate designs, and an architecture that is appropriate for one type of computing activity may not be appropriate for all sorts of computing tasks. Edge computing has developed as a feasible and important architecture for distributed computing, allowing computation and storage resources to be deployed closer to the data source, ideally in the same physical area. In general, distributed computing models aren’t new, and the notions of remote offices, branch offices, data center colocation, and cloud computing are well-established.

Decentralization, on the other hand, can be difficult since it necessitates a high level of monitoring and control that is often disregarded when moving away from a traditional centralized computer approach. Edge computing has gained traction as a viable solution to the growing network issues involved with transporting the massive amounts of data that today’s businesses generate and consume. It’s not just a question of quantity, it’s also a question of time; applications increasingly rely on processing and answers that are time-sensitive.

For example, the rise of self-driving automobiles. Intelligent traffic control signals will be required. Automobiles and traffic control systems will have to produce, analyze, and share data in real time. When you multiply this requirement by a large number of autonomous vehicles, you can see the scope of the possible issues. This necessitates a network that is both quick and responsive.

Some of the major use cases for edge computing 

Edge computing approaches are used to gather, filter, process, and analyze data in-place at or near the network edge. It’s a powerful way to utilize data that can’t be moved to a centralized location first because the sheer volume of data makes such movements prohibitively expensive, technologically difficult, or would otherwise breach compliance standards like data sovereignty. This term has inspired a slew of real-life examples and applications:

Manufacturing

Edge computing was used by an industrial company to monitor manufacturing, allowing real-time analytics and machine learning to be performed at the edge to detect production mistakes and enhance product quality.The deployment of environmental sensors across the production plant was aided by edge computing, which provided insight into how each product component is assembled and stored, as well as how long the components remain in stock. The factory facility and production operations can now be decided on more quickly and accurately by the producer.

Farming

Consider a company that grows vegetables without the use of sunshine, soil, or pesticides indoors. The method reduces the time it takes for plants to grow by more than 60%. The company can track water use, nutritional density, and estimate the best harvest time by using sensors. Data is gathered and evaluated in order to determine the effects of environmental conditions, improve crop-growing algorithms, and ensure that crops are harvested in optimal condition.

Optimization of the network

By measuring network performance for users throughout the internet and using analytics to discover the most reliable, low-latency network path for each user’s data, edge computing can assist in optimizing network performance. In effect, edge computing is used to “steer” traffic around the network in order to get the best possible performance for time-sensitive traffic.

Secure Workplace

Edge computing can combine and analyze data from on-site cameras, employee safety devices, and a variety of other sensors to help businesses monitor workplace conditions or ensure that employees follow established safety protocols, particularly in remote or unusually dangerous environments like construction sites or oil rigs.