Edge computing brings computing closer to the data source, reducing delays and data transfer. It involves running fewer processes in the cloud and moving them to local devices or edge servers. By minimizing long-distance communication between clients and servers, computation is brought to the network’s edge, which is physically close to the devices being used.
Edge computing differs from other computing models such as early centralized computing, decentralized personal computing, and centralized cloud computing. While cloud computing offers centralized services accessible from any device, it can introduce latency due to the distance between users and data centers. Edge computing overcomes this by running applications closer to users while retaining the centralized nature of the cloud.
An example of edge computing is securing a building with IoT video cameras. Instead of streaming all the footage to a cloud server for processing, each camera runs a motion-detection application locally and sends only the relevant footage to the server, as opposed to relaying all of it. This reduces bandwidth usage and server load. Edge computing has various use cases, including security systems, IoT devices, self-driving cars, medical monitoring, and video conferencing, among others, where the delay in communications with a distant data center would prove frustrating, if not fatal.
As a result of the reduction of data transfer, edge computing provides benefits such as cost savings, improved performance, and the ability to offer new functionality. However, it also introduces potential drawbacks like increased attack vectors and the need for additional local hardware. Edge servers can help mitigate some of these challenges by distributing edge computing capabilities across multiple locations.
Looking to learn more? We suggest heading over to Cloudflare’s Learning Center for an in-depth look at edge computing.
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