Edge computing refers to a distributed computing paradigm where data processing occurs closer to the location where it is generated (the "edge" of the network), rather than relying solely on centralized cloud data centers. In this model, data is processed locally on devices or edge servers, reducing latency, improving response times, and optimizing bandwidth usage.

Edge computing is especially relevant for applications that require real-time data processing and low latency, such as IoT (Internet of Things) devices, autonomous vehicles, smart cities, and industrial automation. For example, in autonomous driving, edge computing enables vehicles to make immediate decisions, such as obstacle detection and avoidance, without needing to send data to a distant cloud server for processing.

The benefits of edge computing include improved performance, reduced bandwidth costs, enhanced security by keeping sensitive data closer to its source, and the ability to function independently of a central data center in case of network disruptions.

Edge computing complements cloud computing by handling time-sensitive data at the edge while sending non-critical data to the cloud for long-term storage or in-depth analysis.