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Edge computing

Edge computing is a distributed computing model that brings computation and data storage closer to the sources of data, so that a user is likely to be physically closer to a server than if all servers were in one place. This often makes applications faster.[1] More broadly, it refers to any design that pushes computation physically closer to a user, so as to reduce the latency compared to when an application runs on a single data centre.[2]

The term began being used in the 1990s to describe content delivery networks—these were used to deliver website and video content from servers located near users.[3] In the early 2000s, these systems expanded their scope to hosting other applications,[4] leading to early edge computing services.[5] These services could do things like find dealers, manage shopping carts, gather real-time data, and place ads.


The Internet of Things (IoT), where devices are connected to the internet, is often linked with edge computing. However, it's important to understand that edge computing and IoT are not the same thing.[6]

Definition[edit]

Edge computing involves running computer programs that deliver quick responses close to where requests are made. Karim Arabi, during an IEEE DAC 2014 keynote[7] and later at an MIT MTL Seminar in 2015, described edge computing as computing that occurs outside the cloud, at the network's edge, particularly for applications needing immediate data processing.[8] Unlike data centers , edge computing environments are not always climate-controlled, despite requiring significant processing power.[9]


Edge computing is often equated with fog computing, particularly in smaller setups.[10] However, in larger deployments, such as smart cities, fog computing serves as a distinct layer between edge computing and cloud computing, with each layer having its own responsibilities.[11][12]


"The State of the Edge" report explains that edge computing focuses on servers located close to the end-users.[13] Alex Reznik, Chair of the ETSI MEC ISG standards committee, defines 'edge' loosely as anything that's not a traditional data center.[14]


In cloud gaming, edge nodes, known as "gamelets," are typically within one or two network hops from the client, ensuring quick response times for real-time games.[15]


Edge computing might use virtualization technology to simplify deploying and managing various applications on edge servers.[16]

Applications[edit]

Edge application services reduce the volumes of data that must be moved, the consequent traffic, and the distance that data must travel. That provides lower latency and reduces transmission costs. Computation offloading for real-time applications, such as facial recognition algorithms, showed considerable improvements in response times, as demonstrated in early research.[29] Further research showed that using resource-rich machines called cloudlets or micro data centers near mobile users, which offer services typically found in the cloud, provided improvements in execution time when some of the tasks are offloaded to the edge node.[30] On the other hand, offloading every task may result in a slowdown due to transfer times between device and nodes, so depending on the workload, an optimal configuration can be defined.


IoT-based power grid system enables communication of electricity and data to monitor and control the power grid,[31] which makes energy management more efficient.


Other notable applications include connected cars, autonomous cars,[32] smart cities,[33] Industry 4.0, home automation[34] and satellite systems.[35] The nascent field of edge artificial intelligence (edge AI) implements the artificial intelligence in an edge computing environment, on the device or close to where data is collected.[36]