Citation

Siew, Hong Wei and Tan, Saw Chin and Lee, Ching Kwang (2021) Hybrid SDN Deployment Using Machine Learning. In: 7th International Conference on Computational Science and Technology, ICCST 2020, 29 - 30 August 2020, Pattaya, Thailand.

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Abstract

Software-Defined Networking (SDN) has attracted tremendous attention in recent years as the future communication network architecture. However, SDN deployment in legacy network will be progressively phased over a period, especially for larger network which consists of hundred or more nodes. Every migration (i.e. replacing or upgrading) of SDN-enabled nodes requires considerable optimization efforts in terms of cost of investment, network stability and performance gains. Hitherto literatures have proposed variety of static heuristic algorithms to compute the migration sequence of SDN-enabled nodes for multi-periods SDN deployment in legacy network. The aim of each computed migration sequence is aims to improve network performance gains with respect to address different constraints. However, the dynamicity of an unique network, such as traffic growth or topology change, cannot be comprehensively addressed using a static heuristic algorithm over the deployment duration. Machine learning (ML), on the other hand, has been proven successfully applied for various dynamic and non-linear problems in diverse domains. In this article, we summarize the generic workflow for ML in networking domain at first. Subsequently, we investigated the problem of SDN deployment in legacy network from the perspective of ML. We proposed a SDN deployment problem that formulated as Markov Decision Process and reinforcement learning techniques, such as Qlearning and SARSA, can be used to model for the problem.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Machine learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Computing and Informatics (FCI) Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	28 Apr 2021 18:26
Last Modified:	28 Apr 2021 18:26
URII:	http://shdl.mmu.edu.my/id/eprint/8628

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