Journal of Machine and Computing


Definition and Applications of SDN, NFV, Edge Computing and AI/ML Techniques



Journal of Machine and Computing

Received On : 25 January 2022

Revised On : 18 March 2022

Accepted On : 10 May 2022

Published On : 05 July 2022

Volume 02, Issue 03

Pages : 103-113


Abstract


A surge in Artificial Intelligence (AI) services and applications has been spurred by advances in deep learning. Massive data generation at the network edge is being sparked by the fast advancements in mobile computing and Artificial Intelligence of Things (AIoT). Big data can only be completely realized if the AI frontiers are pushed to the network edge, propelled by the successes of AI and IoT. It is hoped that Edge Computing would help to fulfil this trend by supporting AI applications that are computationally heavy on edge devices. Machine learning algorithms may be deployed to the end devices in which the data is created thanks to Edge AI. For every individual and business, Edge Intelligence has the ability to give AI at any moment, any place. This paper is limited to evaluating the definitions, history and applications of Software Defined Networks (SDNs), Network Functions Virtualization (NFV), Edge Computing (EC), Artificial Intelligence (AI)/Machine Learning (ML) techniques.


Keywords


Edge Computing (EC), Software-Defined Network (SDNs), Network Functions Virtualization (NFV), Artificial Intelligence (AI), Machine Learning (ML)


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Acknowledgements


Author(s) thanks to London Global University for research lab and equipment support.


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No funding was received to assist with the preparation of this manuscript.


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Cite this article


Fabio Caccioli Capra, “Definition and Applications of SDN, NFV, Edge Computing and AI/ML Techniques”, Journal of Machine and Computing, vol.2, no.3, pp. 103-113, July 2022. doi: 10.53759/7669/jmc202202015.


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© 2022 Fabio Caccioli Capra. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.