Load Balancing Algorithm of Controller Based on SDN Architecture Under Machine Learning

Liang Siyuan, Jiang Wenli (), Zhao Fangli and Zhao Feng
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Liang Siyuan: Shaanxi Key Laboratory of Information Communication Network and Security, Xi’an University of Posts & Telecommunications, Xi’an710121, China
Jiang Wenli: Shaanxi Key Laboratory of Information Communication Network and Security, Xi’an University of Posts & Telecommunications, Xi’an710121, China
Zhao Fangli: Shaanxi Key Laboratory of Information Communication Network and Security, Xi’an University of Posts & Telecommunications, Xi’an710121, China
Zhao Feng: Shaanxi Key Laboratory of Information Communication Network and Security, Xi’an University of Posts & Telecommunications, Xi’an710121, China

Journal of Systems Science and Information, 2020, vol. 8, issue 6, 578-588

Abstract: With the rapid development of cloud computing and other related services, higher requirements are put forward for network transmission and delay. Due to the inherent distributed characteristics of traditional networks, machine learning technology is difficult to be applied and deployed in network control. The emergence of SDN technology provides new opportunities and challenges for the application of machine learning technology in network management. A load balancing algorithm of Internet of things controller based on data center SDN architecture is proposed. The Bayesian network is used to predict the degree of load congestion, combining reinforcement learning algorithm to make optimal action decision, self-adjusting parameter weight to adjust the controller load congestion, to achieve load balance, improve network security and stability.

Keywords: software defined networks (SDN); Bayesian network; reinforcement learning (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:bpj:jossai:v:8:y:2020:i:6:p:578-588:n:4

DOI: 10.21078/JSSI-2020-578-11

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