Citation

Teh, Jenniffer Sue Ling and Tan, Saw Chin and Wei, Siew Hong and M. Zaki, Muhammad Faiz and Omar, Nazaruddin (2024) Dynamic SDN Multiple Nodes Migration Using SARSA Reinforcement Learning. In: International Conference on Mobile Web and Intelligent Information Systems, 18-20 August 2024, Vienna, Austria.

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Abstract

This article addresses the problem of software-defined Networking (SDN) migration process under dynamic network conditions and present a SARSA-based approach to tackle the challenges associated with it. Traditional SDN migration techniques often fail to adapt effectively to dynamic network environments, leading to suboptimal performance. To overcome these limitations, this article explores the design of State–Action–Reward–State–Action (SARSA) reinforcement learning in SDN migration process. The article presents a comprehensive analysis of the existing literature on SDN migration process and the design of reinforcement learning, highlighting the limitations of current approaches in dynamic network scenarios. It then presents a SARSA-based SDN migration system that utilizes reinforcement learning to adaptively migrate multiple network nodes in response to changing network conditions. Overall, this article gives insightful information into the design of SARSA reinforcement learning in SDN migration process.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Software-defined Networking
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science > QA76.75-76.765 Computer software
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	02 Oct 2024 02:58
Last Modified:	02 Oct 2024 02:58
URII:	http://shdl.mmu.edu.my/id/eprint/13051

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