What Is Edge Computing?
Edge computing is the practice of processing data near the edge of the network where the data is being generated, instead of in a centralized data processing warehouse. Edge computing is a distributed, open IT architecture that features decentralized processing power, enabling mobile computing and IoT technologies. In edge computing, data is processed by the device itself or by a local computer or server, rather than being transmitted to a data centre.
It is done so that real-time data, does not suffer storage issues that can affect an application’s performance and output. It also helps companies save money by allowing the processing done locally, deducting the amount of information that needs to be processed in a cloud-based location.
Edge computing is specially designed to help resolve some of the latency problems caused by cloud computing and getting data to a data center for processing. In those situations, edge computing can work as mini data centers to process time-sensitive information with limited or no connectivity.
Why edge computing?
Edge computing enables data stream acceleration, including real-time data processing without latency. It allows smart applications and devices to respond to data almost instantaneously as it’s being created, eliminating lag time. This is critical for technologies such as self-driving cars and has equally important benefits for business.
Edge computing allows for efficient data processing in that large amounts of data can be processed near the source, reducing Internet bandwidth usage. This both eliminates costs and ensures that applications can be used effectively in remote locations. In addition, the ability to process data without ever putting it into a public cloud adds a useful layer of security for sensitive data.
Examples of edge computing:
Edge computing offers a range of value propositions for smart IoT applications and use cases across a variety of industries. Some of the most popular use cases that will depend on edge computing to deliver improved performance, security and productivity for enterprises include:
- Autonomous vehicles
- Fleet management
- Predictive maintenance
- Voice assistance
