Wikipedia

Edge computing

(Redirected from Edge AI)

Edge computing is a distributed computing model that brings computation and data storage closer to the sources of data. 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 centralized data centre.[1]

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.[2] In the early 2000s, these systems expanded their scope to hosting other applications,[3] leading to early edge computing services.[4] 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.[5]

The edge computing infrastructure

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[6] 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.[7]

Edge computing is often equated with fog computing, particularly in smaller setups.[8] 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.[9][10]

"The State of the Edge" report explains that edge computing focuses on servers located close to the end-users.[11] Alex Reznik, Chair of the ETSI MEC ISG standards committee, defines 'edge' loosely as anything that's not a traditional data center.[12]

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.[13]

Edge computing might use virtualization technology to simplify deploying and managing various applications on edge servers.[14]

Concept

edit

The world's data is expected to grow 61 percent to 175 zettabytes by 2025.[15] According to research firm Gartner, around 10 percent of enterprise-generated data is created and processed outside a traditional centralized data center or cloud. By 2025, the firm predicts that this figure will reach 75 percent.[16] The increase in IoT devices at the edge of the network is producing a massive amount of data — storing and using all that data in cloud data centers pushes network bandwidth requirements to the limit.[17] Despite the improvements in network technology, data centers cannot guarantee acceptable transfer rates and response times, which often is a critical requirement for many applications.[18] Furthermore, devices at the edge constantly consume data coming from the cloud, forcing companies to decentralize data storage 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.[19] By moving services to the edge, it is possible to provide content caching, service delivery, persistent data storage, and IoT management resulting in better response times and transfer rates. At the same time, distributing the logic to different network nodes introduces new issues and challenges.[20]

Privacy and security

edit

The distributed nature of this paradigm introduces a shift in security schemes used in cloud computing. In edge computing, data may travel between different distributed nodes connected through the Internet and thus requires special encryption mechanisms independent of 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.[21] On the other hand, by keeping and processing data at the edge, it is possible to increase privacy by minimizing the transmission of sensitive information to the cloud. Furthermore, the ownership of collected data shifts from service providers to end-users.[22]

Scalability

edit

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.[18]

The state-of-the-art scheduling technique can increase the effective utilization of edge resources and scales the edge server by assigning minimum edge resources to each offloaded task.[23]

Reliability

edit

Management of failovers is crucial in order to keep 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 alert 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 technologies 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.[18] As an example, an edge computing device, such as a voice assistant, may continue to provide service to local users even during cloud service or internet outages.[22]

Speed

edit

Edge computing brings analytical computational resources close to the end users and therefore can increase the responsiveness and throughput of applications. A well-designed edge platform would significantly outperform a traditional cloud-based system. Some applications rely on short response times, making edge computing a significantly more feasible option than cloud computing. Examples range from IoT to autonomous driving,[24] anything health or human / public safety relevant,[25] or involving human perception such as facial recognition, which typically takes a human between 370-620 ms to perform.[26] Edge computing is more likely to be able to mimic the same perception speed as humans, which is useful in applications such as augmented reality where the headset should preferably recognize who a person is at the same time as the wearer does.

Efficiency

edit

Due to the nearness of the analytical resources to the end users, sophisticated analytical tools and Artificial Intelligence tools can run on the edge of the system. This placement at the edge helps to increase operational efficiency and is responsible for many advantages to the system.

Additionally, the usage of edge computing as an intermediate stage between client devices and the wider internet results in efficiency savings that can be demonstrated in the following example: A client device requires computationally intensive processing on video files to be performed on external servers. By using servers located on a local edge network to perform those computations, the video files only need to be transmitted in the local network. Avoiding transmission over the internet results in significant bandwidth savings and therefore increases efficiency.[26] Another example is voice recognition. If the recognition is performed locally, it is possible to send the recognized text to the cloud rather than audio recordings, significantly reducing the amount of required bandwidth.[22]

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.[27] 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.[28] 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,[29] which makes energy management more efficient.

Other notable applications include connected cars, autonomous cars,[30] smart cities,[31] Industry 4.0, home automation[32] and satellite systems.[33] 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.[34]

See also

edit

References

edit
  1. ^ Gartner. "Gartner Trend Insights report 2018" (PDF). Gartner. Archived (PDF) from the original on 2020-12-18. Retrieved 2021-05-26.
  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. ^ 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. S2CID 207181702. 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". Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters - WWW Alt. '04. p. 180. doi:10.1145/1013367.1013397. ISBN 1581139128. S2CID 578337.
  5. ^ Gartner. "2021 Strategic Roadmap for Edge Computing". www.gartner.com. Archived from the original on 2021-03-30. Retrieved 2021-07-11.
  6. ^ "IEEE DAC 2014 Keynote: Mobile Computing Opportunities, Challenges and Technology Drivers". Archived from the original on 2020-07-30. Retrieved 2019-03-25.
  7. ^ MIT MTL Seminar: Trends, Opportunities and Challenges Driving Architecture and Design of Next Generation Mobile Computing and IoT Devices
  8. ^ "What is fog and edge computing?". Capgemini Worldwide. 2017-03-02. Retrieved 2021-07-06.
  9. ^ Dolui, Koustabh; Datta, Soumya Kanti (June 2017). "Comparison of edge computing implementations: Fog computing, cloudlet and mobile edge computing". 2017 Global Internet of Things Summit (GIoTS). pp. 1–6. doi:10.1109/GIOTS.2017.8016213. ISBN 978-1-5090-5873-0. S2CID 11600169.
  10. ^ "Difference Between Edge Computing and Fog Computing". GeeksforGeeks. 2021-11-27. Retrieved 2022-09-11.
  11. ^ "Data at the Edge Report". Seagate Technology.
  12. ^ Reznik, Alex (2018-05-14). "What is Edge?". ETSI - ETSI Blog - etsi.org. Retrieved 2019-02-19. What is 'Edge'? The best that I can do is this: it's anything that's not a 'data center cloud'.
  13. ^ 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). pp. 14–20. doi:10.1109/ICMU.2014.6799051. ISBN 978-1-4799-2231-4. S2CID 10374389.
  14. ^ "Edge virtualization manages the data deluge, but can be complex | TechTarget". IT Operations. Retrieved 2022-12-13.
  15. ^ Patrizio, Andy (2018-12-03). "IDC: Expect 175 zettabytes of data worldwide by 2025". Network World. Retrieved 2021-07-09.
  16. ^ "What We Do and How We Got Here". Gartner. Retrieved 2021-12-21.
  17. ^ Ivkovic, Jovan (2016-07-11). The Methods and Procedures for Accelerating Operations and Queries in Large Database Systems and Data Warehouse (Big Data Systems) (PDF). National Repository of Dissertations in Serbia (Doctoral thesis) (in Serbian and American English).
  18. ^ 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. S2CID 4237186.
  19. ^ Merenda, Massimo; Porcaro, Carlo; Iero, Demetrio (29 April 2020). "Edge Machine Learning for AI-Enabled IoT Devices: A Review". Sensors. 20 (9): 2533. Bibcode:2020Senso..20.2533M. doi:10.3390/s20092533. PMC 7273223. PMID 32365645.
  20. ^ "IoT management". Retrieved 2020-04-08.
  21. ^ 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. hdl:11572/114780.
  22. ^ a b c 3 Advantages of Edge Computing. Aron Brand. Medium.com. Sep 20, 2019
  23. ^ Babar, Mohammad; Sohail Khan, Muhammad (July 2021). "ScalEdge: A framework for scalable edge computing in Internet of things–based smart systems". International Journal of Distributed Sensor Networks. 17 (7): 155014772110353. doi:10.1177/15501477211035332. ISSN 1550-1477. S2CID 236917011.
  24. ^ Liu, S.; Liu, L.; Tang, B. Wu; Wang, J.; Shi, W. (2019). "Edge Computing for Autonomous Driving: Opportunities and Challenges". Proceedings of the IEEE. 107 (8): 1697–1716. doi:10.1109/JPROC.2019.2915983. S2CID 198311944. Archived from the original on 2021-05-26. Retrieved 2021-05-26.
  25. ^ Yu, W.; et al. (2018). "A Survey on the Edge Computing for the Internet of Things". IEEE Access, vol. 6, pp. 6900-6919. arXiv:2104.01776. doi:10.1109/JIOT.2021.3072611. S2CID 233025108. Archived from the original on 2021-05-26. Retrieved 2021-05-26.
  26. ^ a b Satyanarayanan, Mahadev (January 2017). "The Emergence of Edge Computing". Computer. 50 (1): 30–39. doi:10.1109/MC.2017.9. ISSN 1558-0814. S2CID 12563598.
  27. ^ 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). pp. 73–78. doi:10.1109/HotWeb.2015.22. ISBN 978-1-4673-9688-2. S2CID 6753944.
  28. ^ Verbelen, Tim; Simoens, Pieter; De Turck, Filip; Dhoedt, Bart (2012). "Cloudlets". Proceedings of the third ACM workshop on Mobile cloud computing and services. ACM. pp. 29–36. doi:10.1145/2307849.2307858. hdl:1854/LU-2984272. ISBN 9781450313193. S2CID 3249347. Retrieved 4 July 2019.
  29. ^ Minh, Quy Nguyen; Nguyen, Van-Hau; Quy, Vu Khanh; Ngoc, Le Anh; Chehri, Abdellah; Jeon, Gwanggil (2022). "Edge Computing for IoT-Enabled Smart Grid: The Future of Energy". Energies. 15 (17): 6140. doi:10.3390/en15176140. ISSN 1996-1073.
  30. ^ It's Time to Think Beyond Cloud Computing Published by wired.com retrieved April 10, 2019
  31. ^ 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. S2CID 11163718. Retrieved 5 July 2019.
  32. ^ Chakraborty, T.; Datta, S. K. (November 2017). "Home automation using edge computing and Internet of Things". 2017 IEEE International Symposium on Consumer Electronics (ISCE). pp. 47–49. doi:10.1109/ISCE.2017.8355544. ISBN 978-1-5386-2189-9. S2CID 19156163.
  33. ^ Size of the Prize: How Will Edge Computing in Space Drive Value Creation? Published by Via Satellite retrieved August 18, 2023
  34. ^ "What is edge AI?". www.redhat.com. Retrieved 2023-10-25.
Retrieved from "https://en.wikipedia.org/w/index.php?title=Edge_computing&oldid=1256527805#Applications"