# Introduction > Edge computing is a method of optimizing cloud computing systems by performing data processing at the edge of the network, near the source of the data. This reduces the communications bandwidth needed between sensors and the central datacenter by performing analytics and knowledge generation at or near the source of the data. This approach requires leveraging resources that may not be continuously connected to a network such as laptops, smartphones, tablets and sensors. [Wikipedia](https://en.wikipedia.org/wiki/Edge_computing) > > Fog computing or fog networking, also known as fogging, is an architecture that uses one or more collaborative end-user clients or near-user edge devices to carry out a substantial amount of storage (rather than stored primarily in cloud data centers), communication (rather than routed over the internet backbone), control, configuration, measurement and management (rather than controlled primarily by network gateways such as those in the LTE core network). [Wikipedia](https://en.wikipedia.org/wiki/Fog_computing) > > *Fog Computing* A horizontal, system-level architecture that distributes computing, storage, control and networking functions closer to the users along a cloud-to-thing continuum. [OpenFog Consortium](https://www.openfogconsortium.org/) Open Source Projects * Openstack * OPNFV * OpenDayLight * OpenContrail * ON.Lab * Open Container Initative * Cloud Native Computing Foundation * Open Compute Project --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://theiotlearninginitiative.gitbook.io/edgecomputingsolutions/master.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.