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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.

What is edge computing?

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.

What are the major use cases for edge computing?

Edge computing can complement a hybrid computing model, specifically where centralized computing is used for:

  • compute intensive workloads
  • data aggregation and storage
  • artificial intelligence/machine learning
  • coordinating operations across geographies
  • traditional back end processing

Edge computing can also help solve problems at the data source, in near real time. In short, if reduced latency and/or real-time monitoring can support business goals, there is a use case for edge computing.

The Internet of Things (IoT)

For an IoT device, there can be a lot of network steps in between receiving and resolving a request. The more compute power available on the device itself, or at least closer to it in the network, the better the user experience. 

Mobile technologies

When problems arise in mobile computing, they often revolve around latency issues and service failures. Edge computing can help solve for stringent latency constraints by reducing signal propagation delays. Additionally, it can limit service failures to a smaller area or user population, or provide a degree of service continuity despite intermittent network connectivity.


As service providers modernize their networks, they are moving workloads and services out of the core network (in datacenters) towards the network’s edge: around points of presence and central offices. Virtualizing central offices, one of the last physical interfaces for service delivery, will help service providers reach the goal of deploying services at the network edge.

Keep exploring edge computing

Boosting manufacturing efficiency and product quality with AI/ML, edge computing and Kubernetes

Maintaining order at the edge: Why cluster management is critical to edge computing

Your open source foundation for edge computing

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.

There's a lot more to do with edge computing