Edge computing

Edge Computing is pushing the frontier of computing applications, data, and services away from centralized nodes to the logical extremes of a network.[1] It enables analytics and knowledge generation to occur at 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.[2] 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,[3] mobile edge computing,[4][5] cloudlet, distributed data storage and retrieval, autonomic self-healing networks, remote cloud services, augmented reality, and more.[6]

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 web servers, which may be vast and include many networks. As a topological paradigm, edge computing is also referred to as mesh computing, peer-to-peer computing, autonomic (self-healing) computing, grid computing, and 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, technology advancement and cost reduction for large-scale implementations have made the technology available to small and medium-sized business.

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.

Edge computing has many advantages:

  1. Edge application services significantly decrease the data volume that must be moved, the consequent traffic, and the distance the data must go, 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 is also improved as encrypted data moves further in, toward the network core. As it approaches the enterprise, the 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. Finally, the ability to "virtualize" (i.e., logically group CPU capabilities on an as-needed, real-time basis) extends scalability. The edge computing market is generally based on a "charge for network services" model, and it could be argued 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

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.

See also

References

  1. ↑ Garcia Lopez, Pedro; Montresor, Alberto; Epema, Dick; Datta, Anwitaman; Higashino, Teruo; Iamnitchi, Adriana; Barcellos, Marinho; Felber, Pascal; Riviere, Etienne (2015-09-01). "Edge-centric Computing: Vision and Challenges". SIGCOMM Comput. Commun. Rev. 45 (5): 37–42. doi:10.1145/2831347.2831354. ISSN 0146-4833.
  2. ↑ 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.
  3. ↑ 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 March 2016.
  4. ↑ "Mobile-Edge-Computing White Paper" (PDF). ETSI.
  5. ↑ Ahmed, Arif; Ahmed, Ejaz. A Survey on Mobile Edge Computing. India: 10th IEEE International Conference on Intelligent Systems and Control(ISCO’16), India.
  6. ↑ Edge Computing - Pacific Northwest National Laboratory

External links

Companies providing edge computing services

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