Edge Computing Ecosystem Architecture, Use Cases, and Examples
Edge computing brings AI and analytics workloads closer to where data is created, and actions are taken—reducing latency, decreasing network demands, increasing the privacy of personal and sensitive information, and improving operational resilience.
- Rob High, IBM Fellow, VP & CTO, Networking & Edge Computing, IBM Cloud
The edge architecture is a distributed computing architecture that incorporates every component active in edge computing—everything from devices to sensors to servers to clouds—wherever data is handled or utilized in remote areas of the network.
Using the term "edge computing" connotes a physical location. Instead of depending on the "bigger" clouds to perform all the work, it is computing that is done at or near the source of the data. An advantage of edge computing is that it brings corporate applications closer to the areas where data is generated and action is required. What is the source of all this information?
Devices that govern data flow at the network boundary are edge devices. With the rise of cloud computing and the Internet of Things (IoT), the importance of edge devices has increased, necessitating more intelligence, computation, and AI services
