A Q-learning-based approach for deploying dynamic service function chains

Jian Sun, Guanhua Huang, Gang Sun*, Hongfang Yu, Arun Kumar Sangaiah, Victor Chang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

32 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number646
JournalSymmetry
Volume10
Issue number11
DOIs
Publication statusPublished - 16 Nov 2018

Keywords

  • Load balancing
  • Network function virtualization
  • Reinforcement learning
  • Security
  • Service function chain

Fingerprint

Dive into the research topics of 'A Q-learning-based approach for deploying dynamic service function chains'. Together they form a unique fingerprint.

Cite this