基于Q-learning的跨域服务链映射机制

张红旗; 黄睿; 杨英杰; 常德显; 张连成

doi:10.11959/j.issn.1000-436x.2018283

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基于Q-learning的跨域服务链映射机制

学术论文 | 更新时间：2024-06-05

- 基于Q-learning的跨域服务链映射机制
- Cross-domain service chain mapping mechanism based on Q-learning
- 通信学报 2018年39卷第12期页码：102-112
- 作者机构：
  
  中国人民解放军战略支援部队信息工程大学密码工程学院，河南郑州 450001
- 作者简介：
  
  [ "张红旗（1962–），男，河北唐山人，博士，中国人民解放军战略支援部队信息工程大学教授、博士生导师，主要研究方向为网络与信息安全。" ]
  [ "黄睿（1993–），女，新疆乌鲁木齐人，中国人民解放军战略支援部队信息工程大学硕士生，主要研究方向为软件定义网络、网络功能虚拟化、网络安全。" ]
  [ "杨英杰（1971–），男，河南郑州人，博士，中国人民解放军战略支援部队信息工程大学教授、硕士生导师，主要研究方向为网络与信息安全。" ]
  [ "常德显（1977–），男，河南南阳人，博士，中国人民解放军战略支援部队信息工程大学副教授、讲师，主要研究方向为网络与信息安全。" ]
  [ "张连成（1982–），男，河南商丘人，博士，中国人民解放军战略支援部队信息工程大学副教授、讲师，主要研究方向为软件定义网络。" ]
- 基金信息：
  
  国家高技术研究发展计划（“863”计划）基金资助项目(No.2012AA012704);国家自然科学基金资助项目(No.61402526);国家自然科学基金资助项目(No.61402525);国家自然科学基金资助项目(No.61502528);郑州市科技领军人才项目(No.131PLJRC644)
- DOI：10.11959/j.issn.1000-436x.2018283
  中图分类号： TP393
- 网络出版日期：2018-12，
  
  纸质出版日期：2018-12-25
- 稿件说明：
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张红旗, 黄睿, 杨英杰, 等. 基于Q-learning的跨域服务链映射机制[J]. 通信学报, 2018,39(12):102-112.

Hongqi ZHANG, Rui HUANG, Yingjie YANG, et al. Cross-domain service chain mapping mechanism based on Q-learning[J]. Journal on communications, 2018, 39(12): 102-112.
张红旗, 黄睿, 杨英杰, 等. 基于Q-learning的跨域服务链映射机制[J]. 通信学报, 2018,39(12):102-112. DOI： 10.11959/j.issn.1000-436x.2018283.

Hongqi ZHANG, Rui HUANG, Yingjie YANG, et al. Cross-domain service chain mapping mechanism based on Q-learning[J]. Journal on communications, 2018, 39(12): 102-112. DOI： 10.11959/j.issn.1000-436x.2018283.

摘要

针对软件定义网络功能虚拟化环境下跨域服务链映射问题，提出了一种区域集中管理、全局协同调度的虚拟服务资源管控架构。在此基础上，建立了一种有效的跨域服务链映射框架，在此框架下将跨域服务链映射问题建模为以最小化映射开销为目标的整数线性规划问题，并基于Q-learning机制设计跨域服务链构建请求分割算法进行优化求解。仿真实验表明该方法在平均分割时间、平均映射开销和服务链构建请求接受率等方面相较传统方法具有更优的表现。

Abstract

A virtual service resources controlling architecture with regional centralized management and global coordinated scheduling was proposed for the problem of cross-domain service chain mapping in SDNFV environment. On this basis

an effective mapping framework was built and the cross-domain mapping problem was modeled as an ILP with the purpose of minimizing mapping cost. A partitioning algorithm was designed to solve the problem based on Q-learning mechanism under this framework. Simulation results show that the performances of this method are better than other traditional methods on average partition time

average mapping cost

and acceptance ratioof service chain mapping request.

关键词

Keywords

references

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