Survey on Machine Learning Algorithms for SDN/NFV Automation 


Vol. 44,  No. 1, pp. 92-105, Jan.  2019
10.7840/kics.2019.44.1.92


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  Abstract

As applications of network become popular and the number of the end devices increases, modern network needs to cover more intensive demands compared to traditional networks. Since networks such as 5G that aim to guarantee massive connectivity, high data rate, and ultra-low latency, have a limitation based on hardware-based architectures, it has been proposed to deploy software-based programmable SDN and NFV architectures. By deploying SDN and NFV, it has been proposed to adopt machine learning algorithms to automatically control SDN and NFV, leading to intelligent networks. In this paper, we extensively review and summarize prior works on machine learning based SDN/NFV network management and orchestration. Also, we discuss limitation of prior works and direction for future research.

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  Cite this article

[IEEE Style]

S. Cho, D. Jung, S. Lee, M. Shin, H. Park, "Survey on Machine Learning Algorithms for SDN/NFV Automation," The Journal of Korean Institute of Communications and Information Sciences, vol. 44, no. 1, pp. 92-105, 2019. DOI: 10.7840/kics.2019.44.1.92.

[ACM Style]

Sunwoo Cho, Daeun Jung, Soohwan Lee, Myung-Ki Shin, and Hyunggon Park. 2019. Survey on Machine Learning Algorithms for SDN/NFV Automation. The Journal of Korean Institute of Communications and Information Sciences, 44, 1, (2019), 92-105. DOI: 10.7840/kics.2019.44.1.92.

[KICS Style]

Sunwoo Cho, Daeun Jung, Soohwan Lee, Myung-Ki Shin, Hyunggon Park, "Survey on Machine Learning Algorithms for SDN/NFV Automation," The Journal of Korean Institute of Communications and Information Sciences, vol. 44, no. 1, pp. 92-105, 1. 2019. (https://doi.org/10.7840/kics.2019.44.1.92)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880