Understand how edge computing brings computation closer to users for lower latency.
Edge computing is a distributed computing paradigm that brings computation and data storage closer to the location where it is needed, to improve response times and save bandwidth. Instead of sending data from an IoT device or a user's phone all the way to a centralized cloud for processing, that processing happens 'at the edge' of the network, closer to the user. The 'edge' can be a local device, a nearby cell tower, or a local data center. This model addresses the limitations of traditional cloud computing for certain applications. For example, in applications requiring real-time responses, like self-driving cars or augmented reality, the latency of a round trip to a distant cloud data center is unacceptable. By processing data locally, edge computing can provide near-instantaneous feedback. It also reduces bandwidth costs by pre-processing data at the edge and only sending the important, summarized results to the central cloud for long-term storage or further analysis. This is particularly useful for IoT deployments with thousands of sensors generating vast amounts of data. Cloud providers are extending their platforms to the edge with services like AWS Wavelength (which embeds compute in 5G networks) and Google Distributed Cloud Edge, blurring the lines between the central cloud and the edge.