基于深度强化学习的软件定义网络QoS优化

兰巨龙; 张学帅; 胡宇翔; 孙鹏浩

doi:10.11959/j.issn.1000-436x.2019227

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基于深度强化学习的软件定义网络QoS优化

学术论文 | 更新时间：2024-06-05

- 基于深度强化学习的软件定义网络QoS优化
- Software-defined networking QoS optimization based on deep reinforcement learning
- 通信学报 2019年40卷第12期页码：60-67
- 作者机构：
  
  国家数字交换系统工程技术研究中心，河南郑州 450001
- 作者简介：
  
  [ "兰巨龙（1962- ），男，河北张家口人，博士，国家数字交换系统工程技术研究中心教授、博士生导师，主要研究方向为未来信息通信网络关键理论与技术" ]
  [ "张学帅（1994- ），男，山东菏泽人，国家数字交换系统工程技术研究中心硕士生，主要研究方向为软件定义网络" ]
  [ "胡宇翔（1982- ），男，河南周口人，博士，国家数字交换系统工程技术研究中心副教授、博士生导师，主要研究方向为未来网络关键技术、网络智慧化等" ]
  [ "孙鹏浩（1992- ），男，山东青岛人，国家数字交换系统工程技术研究中心博士生，主要研究方向为软件定义网络、流量工程等" ]
- 基金信息：
  
  国家网络空间安全专项基金资助项目(2017YFB0803204);国家自然科学基金创新群体基金资助项目(61521003);国家自然科学基金资助项目(61702547);国家自然科学基金资助项目(61872382);广东省重点领域研发计划基金资助项目(2018B010113001)
- DOI：10.11959/j.issn.1000-436x.2019227
  中图分类号： TP393
- 网络出版日期：2019-12，
  
  纸质出版日期：2019-12-25
- 稿件说明：
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兰巨龙, 张学帅, 胡宇翔, 等. 基于深度强化学习的软件定义网络QoS优化[J]. 通信学报, 2019,40(12):60-67.

Julong LAN, Xueshuai ZHANG, Yuxiang HU, et al. Software-defined networking QoS optimization based on deep reinforcement learning[J]. Journal on communications, 2019, 40(12): 60-67.
兰巨龙, 张学帅, 胡宇翔, 等. 基于深度强化学习的软件定义网络QoS优化[J]. 通信学报, 2019,40(12):60-67. DOI： 10.11959/j.issn.1000-436x.2019227.

Julong LAN, Xueshuai ZHANG, Yuxiang HU, et al. Software-defined networking QoS optimization based on deep reinforcement learning[J]. Journal on communications, 2019, 40(12): 60-67. DOI： 10.11959/j.issn.1000-436x.2019227.

摘要

为解决软件定义网络场景中，当前主流的基于启发式算法的QoS优化方案常因参数与网络场景不匹配出现性能下降的问题，提出了基于深度强化学习的软件定义网络QoS优化算法。首先将网络资源和状态信息统一到网络模型中，然后通过长短期记忆网络提升算法的流量感知能力，最后基于深度强化学习生成满足QoS目标的动态流量调度策略。实验结果表明，相对于现有算法，所提算法不但保证了端到端传输时延和分组丢失率，而且提高了22.7%的网络负载均衡程度，增加了8.2%的网络吞吐率。

Abstract

To solve the problem that the QoS optimization schemes which based on heuristic algorithm degraded often due to the mismatch between parameters and network characteristics in software-defined networking scenarios

a software-defined networking QoS optimization algorithm based on deep reinforcement learning was proposed.Firstly

the network resources and state information were integrated into the network model

and then the flow perception capability was improved by the long short-term memory

and finally the dynamic flow scheduling strategy

which satisfied the specific QoS objectives

were generated in combination with deep reinforcement learning.The experimental results show that

compared with the existing algorithms

the proposed algorithm not only ensures the end-to-end delay and packet loss rate

but also improves the network load balancing by 22.7% and increases the throughput by 8.2%.

关键词

Keywords

references

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