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基于改进深度强化学习的虚拟网络功能部署优化算法

唐伦 贺兰钦 连沁怡 谭颀

唐伦, 贺兰钦, 连沁怡, 谭颀. 基于改进深度强化学习的虚拟网络功能部署优化算法[J]. 电子与信息学报, 2021, 43(6): 1724-1732. doi: 10.11999/JEIT200297
引用本文: 唐伦, 贺兰钦, 连沁怡, 谭颀. 基于改进深度强化学习的虚拟网络功能部署优化算法[J]. 电子与信息学报, 2021, 43(6): 1724-1732. doi: 10.11999/JEIT200297
Lun TANG, Lanqin HE, Qinyi LIAN, Qi TAN. Virtual Network Function Placement Optimization Algorithm Based on Improve Deep Reinforcement Learning[J]. Journal of Electronics & Information Technology, 2021, 43(6): 1724-1732. doi: 10.11999/JEIT200297
Citation: Lun TANG, Lanqin HE, Qinyi LIAN, Qi TAN. Virtual Network Function Placement Optimization Algorithm Based on Improve Deep Reinforcement Learning[J]. Journal of Electronics & Information Technology, 2021, 43(6): 1724-1732. doi: 10.11999/JEIT200297

基于改进深度强化学习的虚拟网络功能部署优化算法

doi: 10.11999/JEIT200297
基金项目: 国家自然科学基金(62071078),重庆市教委科学技术研究项目(KJZD-M201800601),重庆市重大主题专项 (cstc2019jscx-zdztzxX0006)
详细信息
    作者简介:

    唐伦:男,1973年生,教授,博士,研究方向为下一代无线通信网络、异构蜂窝网络、软件定义无线网络等

    贺兰钦:男,1995年生,硕士生,研究方向为5G网络切片、机器学习算法

    谭颀:女,1995年生,硕士生,研究方向为5G网络切片、资源分配、随机优化理论

    通讯作者:

    贺兰钦 719097886@qq.com

  • 中图分类号: TN929.5

Virtual Network Function Placement Optimization Algorithm Based on Improve Deep Reinforcement Learning

Funds: The National Natural Science Foundation of China (62071078), The Science and Technology Research Program of Chongqing Municipal Education Commission (KJZD-M201800601), The Major Theme Special Projects of Chongqing (cstc2019jscx-zdztzxX0006)
  • 摘要: 针对网络功能虚拟化/软件定义网络 (NFV/SDN)架构下,网络服务请求动态到达引起的服务功能链(SFC)部署优化问题,该文提出一种基于改进深度强化学习的虚拟网络功能(VNF)部署优化算法。首先,建立了马尔科夫决策过程 (MDP)的随机优化模型,完成SFC的在线部署以及资源的动态分配,该模型联合优化SFC部署成本和时延成本,同时受限于SFC的时延以及物理资源约束。其次,在VNF部署和资源分配的过程中,存在状态和动作空间过大,以及状态转移概率未知等问题,该文提出了一种基于深度强化学习的VNF智能部署算法,从而得到近似最优的VNF部署策略和资源分配策略。最后,针对深度强化学习代理通过ε贪婪策略进行动作探索和利用,造成算法收敛速度慢等问题,提出了一种基于值函数差异的动作探索和利用方法,并进一步采用双重经验回放池,解决经验样本利用率低的问题。仿真结果表示,该算法能够加快神经网络收敛速度,并且可以同时优化SFC部署成本和SFC端到端时延。
  • 图  1  系统模型

    图  2  改进深度强化学习算法框架

    图  3  损失函数对比

    图  4  系统总时延对比

    图  5  部署成本对比

    图  6  效用对比

    表  1  网络场景的仿真参数

    仿真参数仿真参数
    数据包到达过程泊松过程${\lambda _i} = 2$数据包大小500 kByte/packet
    通用服务器总台数$H$6台物理链路带宽资源640 MB
    通用服务器$v$的CPU资源容量8核单个CPU服务速率$\beta $25 MB/s
    折扣因子$\gamma $0.99软更新因子$\tau $0.01
    最大迭代轮数2000学习率$\alpha $$\left\{ {0.00001,0.0001} \right\}$
    SFC的长度Uniform[2,5]个SFC的时延最长限制${D_i}$30 ms
    正数$\partial $30正数$\varsigma $20
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-04-21
  • 修回日期:  2021-01-22
  • 网络出版日期:  2021-01-29
  • 刊出日期:  2021-06-18

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