Edge computing

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Edge computing is a method of optimising 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 datacentre 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.[1] 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,[2] mobile edge computing,[3][4] cloudlet, distributed data storage and retrieval, autonomic self-healing networks, remote cloud services, augmented reality, and more.[5]

Overview[edit]

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 web servers, which may spread over a vast area. As a technological paradigm, edge computing is also referred to[by whom?] as mesh computing, peer-to-peer computing, autonomic (self-healing) computing, grid computing, and by other names implying non-centralized, nodeless availability.

To ensure acceptable performance of widely dispersed distributed services, large organizations typically implement edge computing by deploying Web server farms with clustering. 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.[citation needed]

The target end-user is any Internet client making use of commercial Internet application services.

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.[6]

Possible advantages of edge computing are:

  1. 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).
  2. Edge computing eliminates, or at least de-emphasizes, the core computing environment, limiting or removing a major bottleneck and a potential point of failure.
  3. Security improves as encrypted data moves further in, toward the network core. As it approaches the enterprise, data is checked as it passes through protected firewalls and other security points, where viruses, compromised data, and active hackers can be caught early on.
  4. The ability to "virtualize" (i.e., logically group CPU capabilities on an as-needed, real-time basis) extends scalability. The edge-computing market generally operates basically on a "charge for network services" model, and it could be argued[original research?] that typical customers for edge services are organizations desiring linear scale of business application performance to the growth of, e.g., a subscriber base.

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[edit]

Edge computing and grid computing are related. Whereas grid computing would be hard-coded into a specific application to distribute its complex and resource intensive computational needs across a global grid of cheap networked machines, edge computing provides a generic template facility for any type of application to spread its execution across a dedicated grid of prepared expensive machines.[citation needed]

See also[edit]

References[edit]

  1. ^ Gaber, Mohamed Medhat; Stahl, Frederic; Gomes, Joao Bártolo (2014). Pocket Data Mining - Big Data on Small Devices (1 ed.). Springer International Publishing. ISBN 978-3-319-02710-4. 
  2. ^ Skala, Karolj; Davidović, Davor; Afgan, Enis; Sović, Ivan; Šojat, Zorislav (2015). "Scalable Distributed Computing Hierarchy: Cloud, Fog and Dew Computing". Open Journal of Cloud Computing (OJCC). RonPub. 2 (1): 16–24. ISSN 2199-1987. Retrieved 1 March 2016. 
  3. ^ "Mobile-Edge-Computing White Paper" (PDF). ETSI. 
  4. ^ Ahmed, Arif; Ahmed, Ejaz. A Survey on Mobile Edge Computing. India: 10th IEEE International Conference on Intelligent Systems and Control(ISCO’16), India. 
  5. ^ Edge Computing - Pacific Northwest National Laboratory
  6. ^ Gai, Keke; Meikang Qiu; Hui Zhao; Lixin Tao; Ziliang Zong (2016). "Mobile cloud computing: Dynamic energy-aware cloudlet-based mobile cloud computing model for green computing". 59: 46–54. doi:10.1016/j.jnca.2015.05.016.  PDF