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Article

A Q-Learning-Based Approach for Deploying Dynamic Service Function Chains

1
Key Lab of Optical Fiber Sensing and Communications (Ministry of Education), University of Electronic Science and Technology of China, Chengdu 611731, China
2
Center for Cyber Security, University of Electronic Science and Technology of China, Chengdu 611731, China
3
School of Computing Science and Engineering, Vellore Institute of Technology, Tamil Nadu 632014, India
4
International Business School Suzhou (IBSS), Xi’an Jiaotong-Liverpool University, Suzhou 215123, China
*
Author to whom correspondence should be addressed.
Symmetry 2018, 10(11), 646; https://doi.org/10.3390/sym10110646
Received: 12 October 2018 / Revised: 5 November 2018 / Accepted: 14 November 2018 / Published: 16 November 2018
(This article belongs to the Special Issue Symmetry-Adapted Machine Learning for Information Security)
As the size and service requirements of today’s networks gradually increase, large numbers of proprietary devices are deployed, which leads to network complexity, information security crises and makes network service and service provider management increasingly difficult. Network function virtualization (NFV) technology is one solution to this problem. NFV separates network functions from hardware and deploys them as software on a common server. NFV can be used to improve service flexibility and isolate the services provided for each user, thus guaranteeing the security of user data. Therefore, the use of NFV technology includes many problems worth studying. For example, when there is a free choice of network path, one problem is how to choose a service function chain (SFC) that both meets the requirements and offers the service provider maximum profit. Most existing solutions are heuristic algorithms with high time efficiency, or integer linear programming (ILP) algorithms with high accuracy. It’s necessary to design an algorithm that symmetrically considers both time efficiency and accuracy. In this paper, we propose the Q-learning Framework Hybrid Module algorithm (QLFHM), which includes reinforcement learning to solve this SFC deployment problem in dynamic networks. The reinforcement learning module in QLFHM is responsible for the output of alternative paths, while the load balancing module in QLFHM is responsible for picking the optimal solution from them. The results of a comparison simulation experiment on a dynamic network topology show that the proposed algorithm can output the approximate optimal solution in a relatively short time while also considering the network load balance. Thus, it achieves the goal of maximizing the benefit to the service provider. View Full-Text
Keywords: network function virtualization; service function chain; reinforcement learning; load balancing; security network function virtualization; service function chain; reinforcement learning; load balancing; security
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MDPI and ACS Style

Sun, J.; Huang, G.; Sun, G.; Yu, H.; Sangaiah, A.K.; Chang, V. A Q-Learning-Based Approach for Deploying Dynamic Service Function Chains. Symmetry 2018, 10, 646. https://doi.org/10.3390/sym10110646

AMA Style

Sun J, Huang G, Sun G, Yu H, Sangaiah AK, Chang V. A Q-Learning-Based Approach for Deploying Dynamic Service Function Chains. Symmetry. 2018; 10(11):646. https://doi.org/10.3390/sym10110646

Chicago/Turabian Style

Sun, Jian, Guanhua Huang, Gang Sun, Hongfang Yu, Arun K. Sangaiah, and Victor Chang. 2018. "A Q-Learning-Based Approach for Deploying Dynamic Service Function Chains" Symmetry 10, no. 11: 646. https://doi.org/10.3390/sym10110646

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