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Understanding edge computing
Cloud computing has led many organizations to centralize their services within large datacenters. However, new end-user experiences like the Internet of Things (IoT) require service provisioning closer to the outer "edges" of a network, where the physical devices exist.
In a cloud computing model, compute resources and services are often centralized at large datacenters, which are accessed by end users at the "edge" of a network. This model has proven cost advantages and more efficient resource sharing capabilities. However, new forms of end-user experiences like IoT need compute power closer to where a physical device or data source actually exists, i.e. at the network’s "edge."
In response to this, edge computing refers to a model that distributes compute resources out to the "edge" of a network when necessary, while continuing to centralize resources in a cloud model when possible. It is a solution to the problem of needing to quickly provide actionable insights based on time-sensitive data.
No single vendor can provide a complete edge computing solution. Instead, you will assemble a solution from multiple components. Open source platforms ensure interoperability across a wide ecosystem, without the vendor lock-in of a proprietary technology stack. And to enable new edge computing use cases, Red Hat is investing in upstream open source communities like Kubernetes, OpenStack, and Fedora IoT.