Deep Reinforcement Learning Based Migration Mechanism for Service Function Chain in Operator Networks

被引：0
|
作者：
Chen Z. [1 ,2 ]
Feng G. [2 ]
He Y. [2 ]
Zhou Y. [3 ]
机构：
[1] College of Computer Science and Engineering, Chongqing University of Technology, Chongqing
[2] National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu
[3] Department of Computer Science and Software Engineering, Auburn University, Auburn
来源：
Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2020年 / 42卷 / 09期
基金：
中国国家自然科学基金;
关键词：
Deep Reinforcement Learning (DRL); Migration mechanism; Operator network; Service Function Chain (SFC);
D O I：
10.11999/JEITdzyxxxb-42-9-2173
中图分类号：
学科分类号：
摘要：
To improve the service experience provided by the operator network, this paper studies the online migration of Service Function Chain(SFC). Based on the Markov Decision Process(MDP), modeling analysis is performed on the migration of multiple Virtual Network Functions(VNF) in SFC. By combining reinforcement learning and deep neural networks, a double Deep Q-Network(double DQN) based service function chain migration mechanism is proposed. This method can make online migration decisions and avoid over-estimation. Experimental result shows that when compared with the fixed deployment algorithm and the greedy algorithm, the double DQN based SFC migration mechanism has obvious advantages in end-to-end delay and network system revenue, which can help the mobile operator to improve the quality of experience and the efficiency of resources usage.
引用
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页码：2173 / 2179
页数：6
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