Research Article
Research on SDN Enabled by Machine Learning: An Overview
@INPROCEEDINGS{10.1007/978-3-030-63941-9_14, author={Pu Zhao and Wentao Zhao and Qiang Liu}, title={Research on SDN Enabled by Machine Learning: An Overview}, proceedings={6GN for Future Wireless Networks. Third EAI International Conference, 6GN 2020, Tianjin, China, August 15-16, 2020, Proceedings}, proceedings_a={6GN}, year={2021}, month={1}, keywords={Software Defined Networking Machine learning Artificial intelligence}, doi={10.1007/978-3-030-63941-9_14} }- Pu Zhao
Wentao Zhao
Qiang Liu
Year: 2021
Research on SDN Enabled by Machine Learning: An Overview
6GN
Springer
DOI: 10.1007/978-3-030-63941-9_14
Abstract
Network abstraction brings the birth of Software Defined Network (SDN). SDN is a promising network architecture that separates the control logic the network from the underlying forwarding elements. SDN gives network centralized control ability and provides developers with programmable ability. In this review, the latest advances in the field of artificial intelligence (AI) have provided SDN with learning capabilities and superior decision-making capabilities. In this study, we focus on a sub-field of artificial intelligence: machine learning (ML) and give a brief review of recent researches on introducing ML into SDN. Firstly, we introduce the backgrounds of SDN and ML. Then, we conduct a brief review on existing works about how to apply several typical ML algorithms to SDN. Finally, we give conclusion towards integrating SDN with ML.
