Edge computing is a method of optimizing cloud computing systems "by taking the control of computing applications, data, and services away from some central nodes (the "core") to the other logical extreme (the "edge") of the Internet" which makes contact with the physical world. In this architecture, 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 also exploiting relationships those items may have to each other.
This approach requires leveraging resources that may not be continuously connected to a network such as autonomous vehicles, implanted medical devices, fields of highly distributed sensors, and mobile devices. Edge computing covers a wide range of technologies including wireless sensor networks, mobile data acquisition, mobile signature analysis, cooperative distributed peer-to-peer ad hoc networking and processing also classifiable as local cloud/fog computing and grid/mesh computing, dew computing, mobile edge computing, cloudlet, distributed data storage and retrieval, autonomic self-healing networks, remote cloud services, augmented reality, the Internet of Things (IoT) and more. Edge Computing can involve Edge nodes directly attached to physical inputs and output or Edge Clouds that may have such contact but at least exist outside of centralized Clouds closer to the Edge.
Video Edge computing
Overview
Edge computing pushes applications, data and computing power (services) away from centralized points to the logical extremes of a network. Edge computing replicates fragments of information across distributed networks of servers and data stores, which may spread over a vast area. As a technological paradigm, edge computing may be architecturally organized as peer-to-peer computing, autonomic (self-healing) computing, grid computing, and by other names implying non-centralized availability.
To ensure acceptable performance of widely dispersed distributed services, large organizations typically implement edge computing by deploying server farms with clustering and large scale storage networks. Previously available only to very large corporate and government organizations, edge computing has utilized technology advances and cost reductions for large-scale implementations have made the technology available to small and medium-sized businesses while small low cost cluster hardware and freely available cluster management software have made Edge Computing affordable to individual professionals, students, and hobbyists.
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. Edge Computing does use a similar or the same distributed systems architecture as centralized Clouds but closer to or directly at the Edge.
Edge computing imposes certain limitations on the choices of technology platforms, applications or services, all of which need to be specifically developed or configured for edge computing.
Possible advantages of edge computing are:
- Edge application services significantly decrease the volumes of data that must be moved, the consequent traffic, and the distance the data must travel, thereby reducing transmission costs, shrinking latency, and improving quality of service (QoS).
- Edge computing eliminates, or at least de-emphasizes, the core computing environment, limiting or removing a major bottleneck and a potential single point of failure.
- Ability to ride the same cost curves and improvements by exploitation of the same architecture and fundamental underlying computing technologies as other Clouds whether centralized fee-for-service Clouds or closed private clouds which are also centralized. Cost accounting models based upon how shared resources are billed in fee-for-service clouds (timesharing) often expressed by the phrase "as a Service" should not be confused with the common architectural basis of centralized Clouds, Edge Clouds, and increasingly Edge nodes as well. Ultimately all IT systems, distributed or not, must provide viable services regardless of how or where they are implemented. Clouds, however, do share common distributed system architecture and technology forming three modes defined by distance from the edge: Centralized Clouds, Edge Clouds, and Edge nodes taken collectively also known as fog computing.
ISO/IEC 20248 provides a method whereby the data of objects identified by edge computing using Automated Identification Data Carriers [AIDC], a barcode and/or RFID tag, can be read, interpreted, verified and made available into the "Fog" and on the "Edge" even when the AIDC tag has moved on.
Grid computing
Edge computing and grid computing are related. Whereas grid computing is an application overlay on network structure to serve a primary function such as a particular application, Edge computing involves that part of the Internet most directly in touch with sensing or altering the adjacent physical world. Edge computing and grid both form connections of closely related systems and storage but Edge computing interacts on the basis of network connection to physical world input-output and grid computing interconnects strictly on a functional basis.
Maps Edge computing
EDGE 50
In 2018, Data Economy magazine released its inaugural edition of the EDGE 50, a list of the world's first top 50 influencers in edge computing. Among EDGE 50 notables include Richard L. Clemmer (CEO of NXP Semiconductors), Jensen Huang (CEO of Nvidia), Helder Antunes (Chairman of the OpenFog Consortium), and John Krafcik (CEO of Waymo).
See also
- Cloudlet
- Content delivery network
- Fat client
- Mobile edge computing
- Utility computing
- Fog computing
References
Further reading
- Pijush Kanti Dutta Pramanik, Saurabh Pal, Aditya Brahmachari, Prasenjit Choudhury, "Processing IoT Data: From Cloud to Fog. It's Time to be Down-to-Earth," IGI Global, 2018, DOI:10.4018/978-1-5225-4044-1.ch007, https://www.igi-global.com/chapter/processing-iot-data/206593
Source of article : Wikipedia
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