{"id":212082,"date":"2020-02-23T16:44:03","date_gmt":"2015-12-06T22:44:13","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/project\/edge-computing\/"},"modified":"2020-11-12T19:40:46","modified_gmt":"2020-11-13T03:40:46","slug":"edge-computing","status":"publish","type":"msr-project","link":"https:\/\/www.microsoft.com\/en-us\/research\/project\/edge-computing\/","title":{"rendered":"Edge Computing"},"content":{"rendered":"
\n

Edge computing is where compute resources, ranging from credit-card-size computers to micro data centers, are placed closer to information-generation sources, to reduce network latency and bandwidth usage generally associated with cloud computing. Edge computing ensures continuation of service and operation despite intermittent cloud connections. Industries ranging from manufacturing to healthcare are eager to develop real-time control systems that use machine learning and artificial intelligence to improve efficiencies and reduce cost. We are exploring this new computing paradigm by identifying and addressing emerging technology and business model challenges.<\/p>\n

A Brief History of Edge Computing<\/h2>\n

On October 29, 2008, we invited colleagues from academia and industry for a day-long brainstorming session about the future of cloud computing. Edge computing was conceived during those discussions. Attendees included Victor Bahl (organizer, Microsoft Research), Ram\u00f3n C\u00e1ceres (AT&T Labs), Nigel Davies (Lancaster University, U.K.), Mahadev Satyanarayanan (Carnegie Mellon University), and Roy Want (Intel Research). Following this meeting, we published the first paper on this topic, in IEEE Pervasive Computing (November 1, 2009) titled: The Case for VM-based Cloudlets in Mobile Computing<\/a><\/b>.<\/p>\n

The blog Why a Cloudlet Beats the Cloud for Mobile Apps (opens in new tab)<\/span><\/a><\/b> (December 13, 2009) was the first article to cover our ideas. In it are described two projects, Cloudlets, a joint project of Microsoft and Carnegie Mellon University; and MAUI (Mobile Assistance Using Infrastructure), a Microsoft Research project. In Cloudlets, we investigated fast virtual machine (VM) synthesis on the edge; in MAUI, we explored a .NET programming model for computational offloads to the edge. Many of the ideas we explored have withstood the test of time. For example, guarding against network disconnections, incorporating computing versus communications tradeoff, deciding which methods to offload and which to process locally. The papers describing Cloudlet and MAUI have been cited over 5,700 times. Click here<\/a> for a colorful description of a ten year look-back<\/p>\n

Since then, having made the case for edge computing in the research community (see Faculty Summit keynote<\/a><\/b>), industry (see: Network World interview (opens in new tab)<\/span><\/a><\/b>) and internally in Microsoft (see Intelligent Edge<\/a><\/b>), we have been focusing on live-video analytics as the \u201ckiller\u201d app for edge computing. You can read all about it in a separate project page.<\/p>\n

\n

Listen to Victor Bahl’s Podcast,\u00a0 A brief history of networking (and a bit about the future too)<\/em><\/a>,
where he shares some fascinating stories and gives an inside look at Edge Computing.<\/p>\n

\n

The Intelligent Edge<\/h2>\n

Microsoft product groups coined the term The Intelligent Edge. The Intelligent Edge is a capability that enables Microsoft customers to enjoy a seamless experience and compute capabilities wherever their data exists\u2014in the cloud or offline. Microsoft is making it easier for developers to build apps that use edge technology, by open sourcing the Azure IoT Edge Runtime, which allows customers to modify the runtime and customize applications.<\/p>\n

\"Types<\/figure>\n

Learn more about the Intelligent Edge.<\/a><\/p>\n

Recent Activity<\/h2>\n