Edge computing

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Edge computing is a distributed computing paradigm which brings computation and data storage closer to the location where it is needed, to improve response times and save bandwidth.[1] The origins of edge computing lie in content delivery networks that were created in the late 1990s to serve web and video content from edge servers that were deployed close to users.[2] In the early 2000s, these networks evolved to host applications and application components at the edge servers,[3] resulting in the first commercial edge computing services[4] that hosted applications such as dealer locators, shopping carts, real-time data aggregators, and ad insertion engines.[3] Modern edge computing significantly extends this approach through virtualization technology that make it easier to deploy and run a wider range of applications on the edge servers.

The edge computing infrastructure


One definition of edge computing is any type of computer program that delivers low latency nearer to the requests. Karim Arabi, in an IEEE DAC 2014 Keynote [5] and subsequently in an invited talk at MIT's MTL Seminar in 2015 [6] defined edge computing broadly as all computing outside the 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.

According to The State of the Edge report, edge computing concentrates on servers "in close proximity to the last mile network."[citation needed] Alex Reznik, Chair of the ETSI MEC ISG standards committee loosely defines the term: "anything that's not a traditional data center could be the 'edge' to somebody."[7]

Edge nodes used for game streaming are known as gamelets,[8] which are usually one or two hops away from the client.[9] Per Anand and Edwin 'the edge node is mostly one or two hops away from the mobile client to meet the response time constraints for real-time games' in the cloud gaming context.[9]


The increase of IoT devices at the edge of the network is producing a massive amount of data to be computed to data centers, pushing network bandwidth requirements to the limit. Despite the improvements of network technology, data centers cannot guarantee acceptable transfer rates and response times, which could be a critical requirement for many applications.[10] Furthermore, devices at the edge constantly consume data coming from the cloud, forcing companies to build content delivery networks to decentralize data and service provisioning, leveraging physical proximity to the end user. In a similar way, the aim of Edge Computing is to move the computation away from data centers towards the edge of the network, exploiting smart objects, mobile phones or network gateways to perform tasks and provide services on behalf of the cloud. By moving services to the edge, it is possible to provide content caching, service delivery, storage and IoT management resulting in better response times and transfer rates. At the same time, distributing the logic in different network nodes introduces new issues and challenges.

Privacy and security[edit]

The distributed nature of this paradigm introduces a shift in security schemes used in cloud computing. Not only data should be encrypted, but different encryption mechanism should be adopted, since data may transit between different distributed nodes connected through the internet before eventually reaching the cloud. Edge nodes may also be resource constrained devices, limiting the choice in terms of security methods. Moreover, a shift from centralized top-down infrastructure to a decentralized trust model is required.[11] On the other hand, by keeping data at the edge it is possible to shift ownership of collected data from service providers to end-users.


Scalability in a distributed network must face different issues. First, it must take into account the heterogeneity of the devices, having different performance and energy constraints, the highly dynamic condition and the reliability of the connections, compared to more robust infrastructure of cloud data centers. Moreover, security requirements may introduce further latency in the communication between nodes, which may slow down the scaling process.[10]


Management of failovers is crucial in order to maintain a service alive. If a single node goes down and is unreachable, users should still be able to access a service without interruptions. Moreover, edge computing systems must provide actions to recover from a failure and alerting the user about the incident. To this aim, each device must maintain the network topology of the entire distributed system, so that detection of errors and recovery become easily applicable. Other factors that may influence this aspect are the connection technology in use, which may provide different levels of reliability, and the accuracy of the data produced at the edge that could be unreliable due to particular environment conditions.[10]


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.[12] Further research showed that using resource-rich machines called cloudlets near mobile users, offering services typically found in the cloud, provided improvements in execution time when some of the tasks are offloaded to the edge node.[13] 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.

Another use of the architecture is cloud gaming, where some aspects of a game could run in the cloud, while the rendered video is transferred to lightweight clients such as mobile, VR glasses, etc. Such type of streaming is also known as pixel streaming.[8] Conventional cloud games may suffer from high latency and insufficient bandwidth, since the amount of data transferred is huge due to the high resolutions required by some services.[citation needed]

Other notable applications include connected, autonomous cars,[14] smart cities,[15] and home automation systems.[16]

See also[edit]


  1. ^ Hamilton, Eric. "What is Edge Computing: The Network Edge Explained". cloudwards.net. Retrieved 2019-05-14.
  2. ^ "Globally Distributed Content Delivery, by J. Dilley, B. Maggs, J. Parikh, H. Prokop, R. Sitaraman and B. Weihl, IEEE Internet Computing, Volume 6, Issue 5, November 2002" (PDF). Archived (PDF) from the original on 2017-08-09. Retrieved 2019-10-25.
  3. ^ a b Nygren., E.; Sitaraman R. K.; Sun, J. (2010). "The Akamai Network: A Platform for High-Performance Internet Applications" (PDF). ACM SIGOPS Operating Systems Review. 44 (3): 2–19. doi:10.1145/1842733.1842736. Archived (PDF) from the original on September 13, 2012. Retrieved November 19, 2012. See Section 6.2: Distributing Applications to the Edge
  4. ^ Davis, A.; Parikh, J.; Weihl, W. (2004). "EdgeComputing: Extending Enterprise Applications to the Edge of the Internet". 13th International World Wide Web Conference. doi:10.1145/1013367.1013397.
  5. ^ IEEE DAC 2014 Keynote: Mobile Computing Opportunities, Challenges and Technology Drivers
  6. ^ MIT MTL Seminar: Trends, Opportunities and Challenges Driving Architecture and Design of Next Generation Mobile Computing and IoT Devices
  7. ^ "ETSI - ETSI Blog - What is Edge?". www.etsi.org. Retrieved 2019-02-19.
  8. ^ a b "CloudHide: Towards Latency Hiding Techniques for Thin-client Cloud Gaming". ResearchGate. Retrieved 2019-04-12.
  9. ^ a b Anand, B.; Edwin, A. J. Hao (January 2014). "Gamelets — Multiplayer mobile games with distributed micro-clouds". 2014 Seventh International Conference on Mobile Computing and Ubiquitous Networking (ICMU): 14–20. doi:10.1109/ICMU.2014.6799051. ISBN 978-1-4799-2231-4.
  10. ^ a b c Shi, Weisong; Cao, Jie; Zhang, Quan; Li, Youhuizi; Xu, Lanyu (October 2016). "Edge Computing: Vision and Challenges". IEEE Internet of Things Journal. 3 (5): 637–646. doi:10.1109/JIOT.2016.2579198.
  11. ^ Garcia Lopez, Pedro; Montresor, Alberto; Epema, Dick; Datta, Anwitaman; Higashino, Teruo; Iamnitchi, Adriana; Barcellos, Marinho; Felber, Pascal; Riviere, Etienne (30 September 2015). "Edge-centric Computing". ACM SIGCOMM Computer Communication Review. 45 (5): 37–42. doi:10.1145/2831347.2831354.
  12. ^ Yi, S.; Hao, Z.; Qin, Z.; Li, Q. (November 2015). "Fog Computing: Platform and Applications". 2015 Third IEEE Workshop on Hot Topics in Web Systems and Technologies (HotWeb): 73–78. doi:10.1109/HotWeb.2015.22. ISBN 978-1-4673-9688-2. Retrieved 4 July 2019.
  13. ^ Verbelen, Tim; Simoens, Pieter; De Turck, Filip; Dhoedt, Bart (2012). "Cloudlets: Bringing the Cloud to the Mobile User". Proceedings of the Third ACM Workshop on Mobile Cloud Computing and Services. ACM: 29–36. doi:10.1145/2307849.2307858. Retrieved 4 July 2019.
  14. ^ It's Time to Think Beyond Cloud Computing Published by wired.com retrieved April 10, 2019,
  15. ^ Taleb, Tarik; Dutta, Sunny; Ksentini, Adlen; Iqbal, Muddesar; Flinck, Hannu (March 2017). "Mobile Edge Computing Potential in Making Cities Smarter". IEEE Communications Magazine. 55 (3): 38–43. doi:10.1109/MCOM.2017.1600249CM. Retrieved 5 July 2019.
  16. ^ Chakraborty, T.; Datta, S. K. (November 2017). "Home automation using edge computing and Internet of Things". 2017 IEEE International Symposium on Consumer Electronics (ISCE): 47–49. doi:10.1109/ISCE.2017.8355544. ISBN 978-1-5386-2189-9. Retrieved 5 July 2019.