Service chain mapping algorithm based on reinforcement learning

Liang WEI; Tao HUANG; Jiao ZHANG; Zenan WANG; Jiang LIU; Yunjie LIU

doi:10.11959/j.issn.1000-436x.2018002

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Service chain mapping algorithm based on reinforcement learning

Papers | 更新时间：2024-06-05

- Service chain mapping algorithm based on reinforcement learning
- Journal on Communications Vol. 39, Issue 1, Pages: 90-100(2018)
- 作者机构：
  
  北京邮电大学网络与交换技术国家重点实验室，北京 100876
- 作者简介：
- 基金信息：
  
  The National High Technology Research and Development Program of China(863 Program)(2015AA016101);The National Natural Science Foundation of China(61501042);Beijing New-Star Plan of Science and Technology(Z151100000315078)
- DOI：10.11959/j.issn.1000-436x.2018002
  CLC： TP302
- Online First：2018-01，
  
  Published：25 January 2018
- 稿件说明：
移动端阅览
Liang WEI, Tao HUANG, Jiao ZHANG, et al. Service chain mapping algorithm based on reinforcement learning[J]. Journal on Communications, 2018, 39(1): 90-100.
DOI：

Liang WEI, Tao HUANG, Jiao ZHANG, et al. Service chain mapping algorithm based on reinforcement learning[J]. Journal on Communications, 2018, 39(1): 90-100. DOI： 10.11959/j.issn.1000-436x.2018002.

摘要

提出基于人工智能技术的多智能体服务链资源调度架构，设计一种基于强化学习的服务链映射算法。通过Q-learning的机制，根据系统状态、执行部署动作后的奖惩反馈来决定服务链中各虚拟网元的部署位置。实验结果表明，与经典算法相比，该算法有效降低了业务的平均传输延时，提升了系统的负载均衡情况。

Abstract

A service chain resource scheduling architecture of multi-agent based on artificial intelligence technology was proposed.Meanwhile

a service chain mapping algorithm based on reinforcement learning was designed.Through the Q-learning mechanism

the location of each virtual network element in the service chain was determined according to the system status and the reward and punishment feedback after the deployment.The experimental results show that compared with the classical algorithms

the algorithm effectively reduces the average transmission delay of the service and improves the load balance of the system.

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Keywords

references

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