Edge computing is a distributed computing paradigm which brings computer data storage closer to the location where it is needed. Computation is largely or completely performed on distributed device nodes. Edge computing pushes applications, data and computing power (services) away from centralized points to locations closer to the user. The target of edge computing is any application or general functionality needing to be closer to the source of the action where distributed systems technology interacts with the physical world. Edge computing does not need contact with any centralized cloud, although it may interact with one.
Edge application services reduce the volumes of data that must be moved, the consequent traffic, and the distance the data must travel. That provides lower latency and reduces transmission costs. Computation offloading for real-time applications, such as facial recognition, has been demonstrated in early testing.
In computer science, computation offloading is the transfer of resource intensive computational tasks to an external platform, such as a cluster, grid, or a cloud. Offloading may be necessary due to hardware limitations of a devices, such as limited computational power, storage, and energy. The resource intensive tasks may be for searching, virus scanning, image processing, artificial intelligence, computational decision making etc
In one vision of this architecture, specifically for Internet of things (IoT) devices, data comes in from the physical world via various sensors, and actions are taken to change physical state via various forms of output and actuators; by performing analytics and knowledge generation at the edge, communications bandwidth between systems under control and the central data center is reduced. Edge computing takes advantage of proximity to the physical items of interest and also exploits the relationships those items may have to each other.
The Internet of things (IoT) is the extension of Internet connectivity into physical devices and everyday objects. Embedded with electronics, Internet connectivity, and other forms of hardware (such as sensors), these devices can communicate and interact with others over the Internet, and they can be remotely monitored and controlled.
Another vision of the architecture is to give a re-birth for cloud gaming where the game simulations are run in the cloud and the rendered video is transferred to light weight clients such as mobile, VR glasses, etc. Such type of streaming are also known as pixel streaming. Conventional cloud games may suffer from high latency and insufficient bandwidth. As real-time games such as FPS (First person Shooting) games have strict constraints on latency, processing game simulation at the edge node is necessary for the immersive game plays. Edge nodes when used for game streaming, are known as Gamelets, which are usually one or two hops away from the client.
Alex Reznik, Chair of the ETSI MEC ISG standards committee, has a broad definition, “anything that’s not a traditional data center could be the ‘edge’ to somebody.” Other definitions are more limited. The State of the Edge report concentrates on servers “in close proximity to the last mile network.” Gamelet paper defines ‘the edge node is mostly one or two hops away from the mobile client to meet the response time constraints for real-time games’. It also states, “Gamelet system is basically a distributed micro-cloud system in which the computational intensive tasks such as game simulation and rendering are offloaded to a Gamelet node that is few hops (most of the time one or two hops) away from the mobile client”. Philip Laidler believes “edge compute includes workloads running on customer premises.” Some call this the customer, enterprise or device edge. Another, more inclusive way to define “edge computing” is to include any type of computer program delivers low latency nearer to the requests. Karim Arabi, in an IEEE DAC 2014 Keynote and subsequently in an invited talk at MIT’s MTL Seminar in 2015 defined Edge Computing broadly as all computing outside Cloud happening at the Edge of the network and more specifically in applications where real-time processing of data is required. In his definition, Cloud Computing operates on “Big Data” while Edge Computing operates on “Instant Data” that is real-time data generated by sensors or users.
The above is a brief about Edge Computing. Watch this space for more updates on the latest trends in Technology.