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基于深度强化学习的服务功能链可靠部署算法

唐伦 曹睿 廖皓 王兆堃

唐伦, 曹睿, 廖皓, 王兆堃. 基于深度强化学习的服务功能链可靠部署算法[J]. 电子与信息学报, 2020, 42(12): 2931-2938. doi: 10.11999/JEIT190969
引用本文: 唐伦, 曹睿, 廖皓, 王兆堃. 基于深度强化学习的服务功能链可靠部署算法[J]. 电子与信息学报, 2020, 42(12): 2931-2938. doi: 10.11999/JEIT190969
Lun TANG, Rui CAO, Hao LIAO, Zhaokun WANG. Reliable Deployment Algorithm of Service Function Chain Based on Deep Reinforcement Learning[J]. Journal of Electronics and Information Technology, 2020, 42(12): 2931-2938. doi: 10.11999/JEIT190969
Citation: Lun TANG, Rui CAO, Hao LIAO, Zhaokun WANG. Reliable Deployment Algorithm of Service Function Chain Based on Deep Reinforcement Learning[J]. Journal of Electronics and Information Technology, 2020, 42(12): 2931-2938. doi: 10.11999/JEIT190969

基于深度强化学习的服务功能链可靠部署算法

doi: 10.11999/JEIT190969
基金项目: 国家自然科学基金(6157073),重庆市教委科学技术研究项目(KJZD-M201800601)
详细信息
    作者简介:

    唐伦:男,1973年生,教授,博士生导师,研究方向为新一代无线通信网络、异构蜂窝网络、软件定义无线网络等

    曹睿:男,1994年生,硕士生,研究方向为5G网络切片中的资源分配、可靠性

    廖皓:男,1993年生,硕士生,研究方向为5G网络故障检测、自愈合、机器学习

    王兆堃:男,1995年生,硕士生,研究方向为5G网络故障检测、自愈合、机器学习

    通讯作者:

    曹睿 1280139234@qq.com

  • 中图分类号: TN929.5

Reliable Deployment Algorithm of Service Function Chain Based on Deep Reinforcement Learning

Funds: The National Natural Science Foundation of China (61571073), The Science and Technology Research Program of Chongqing Municipal Education Commission (KJZD-M201800601)
  • 摘要:

    针对当前关于服务功能链(SFC)的部署问题都未考虑到虚拟网络功能(VNF)的失效重要度,该文提出了基于深度强化学习的SFC可靠部署算法。首先建立VNF和虚拟链路可靠映射模型,为重要的VNF设置高可靠性需求,并通过链路部署长度限制尽可能保证虚拟链路可靠性需求。其次,以负载均衡为资源协调原则,与VNF可靠性联合优化,最终使用深度强化学习得到服务功能链部署策略。另外,提出了基于重要度的节点备份和链路备份策略,用于应对部署过程中VNF/链路可靠性难以满足的情况。仿真结果表明,该文的可靠部署算法在保证可靠性需求的基础上能够有效减少SFC失效损失,同时使虚拟网络更加稳定可靠。

  • 图  1  系统场景图

    图  2  深度强化算法示意图

    图  3  SFC请求接受率对比

    图  4  SFC备份消耗对比

    图  5  SFC失效损失对比

    图  6  学习率对收敛效果的影响

    表  1  基于深度强化学习的服务功能链可靠部署算法

     算法1 基于深度强化学习的服务功能链可靠部署算法
     经验回放池初始化为空,随机初始化主网络的参数,并复制给目标网络,探寻环境信息基于式(6)~式(9)得到${w_I}$,${u'_k}$
     for episode=1, 2, ···, E do
     初始化状态为${{{s}}_1} = \{ {{{B}}_{{\rm{left}}}}{{,}}{{{C}}_{{\rm{left}}}}{{,}}{{{b}}_{{\rm{map}}}}{{,}}{{{c}}_{{\rm{map}}}}\} $, ${{{b}}_{{\rm{map}}}}{{,}}{{{c}}_{{\rm{map}}}}$为空
     for t=1,2…D do
     确定可选动作集${N'_{k{n_i}}}$, ${N''_{k{n_i}}}$随机产生0到1的数$\tau $
     if $\tau < \varepsilon $:先从${ {{N'} }_{k{n_i} } }$中选择动作${a_t}$,若为空则根据算法2在${ {{N''} }_{k{n_i} } }$中选择${a_t}$,都无动作选择则${a_t}$为空
     else:依据主神经网络估值${\pi ^*}(s) = {{\arg }}\max\limits_{a'} Q({{{s}}_t},{{{a'}}_t})$选取动作。end if
     执行动作${{{a}}_t}$,获得奖励${r_t}$、下一状态${{{s}}_{t + 1}}$,将向量$[{{{s}}_t},{{{a}}_t},{r_t},{{{s}}_{t + 1}}]$放入经验回放池。
     随机取出小批量样本向量$[{{{s}}_t},{{{a}}_t},{r_t},{{{s}}_{t + 1}}]$组成样本向量集${\upsilon _t}$
     for $[{{{s}}_t},{{{a}}_t},{r_t},{{{s}}_{t + 1}}]$ in ${\upsilon _t}$ do
     $L{{(}}\theta {{)}} = {(\tilde Q({{{s}}_t},{{{a}}_t}|{\theta ^ - }) - Q({{{s}}_t},{{{a}}_t}|\theta ))^2}$,$\theta \leftarrow \theta - {\lambda _{{\rm{SGD}}}}\nabla L(\theta )$ end for
     在θ更新T次后将主网络参数复制给目标网络。end for end for
     通过主网络得到每个VNF的策略${\pi ^*}(s)$
     if累计奖励$Q({s_t},{a_t}) < - 100$:拒绝该请求
     else if 部署结果不满足链路可靠性需求:执行算法3 end if
    下载: 导出CSV

    表  2  基于虚拟网络功能重要度节点备份算法

     算法2:基于虚拟网络功能重要度的节点备份算法
     初始化节点部署方案$g'(v_I^k)$为空
     repeat:
     if ${\omega _I} < 1/D$:搜寻${N''_{k{n_i}}}$已作为备份节点的节点集$V_{I'}^k$
     if $V_{I'}^k$非空:$g'(v_I^k)$←从$V_{I'}^k$中寻找备份节点,从${{{N''}}_{k{n_i}}}/V_{I'}^k$中寻
     找部署节点
     else:$g'(v_I^k)$←从${N''_{k{n_i}}}$中选出2个节点。可靠性最高的为部署节
     点,另一备份。endif
     else:$g'(v_I^k)$←从${N''_{k{n_i}}}$中选出2个节点。可靠性最高的为部署节
     点,另一备份。endif
     until $RN(g'(v_I^k)) > {(R_k^{{\rm{req}}})^{{w_I}}}$or 已遍历所有组合
     return 节点部署方案$g'(v_I^k)$
    下载: 导出CSV

    表  3  基于链路备份重要度的链路备份算法

     算法3:基于链路备份重要度的链路备份算法
     基于$\gamma _{I,J}^k$对虚拟链路链路$l_{I,J}^k \in L_v^k$排序
     for 排过序的链路链路$l_{I,J}^k \in L_v^k$
     if 链路可靠性不满足:
     确定$l_{I,J}^k$映射的段物理起始节点${n_i}$和终止节点${n_j}$:${p_{I,J}}$
     $p{{ = \{ }}{\ell _{i,j}}|{L'_{k{l_{i,j}}}}/{p_{I,J}}\} $,部署方案增加链路P
     else: break
     end for
     return 部署方案
    下载: 导出CSV
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出版历程
  • 收稿日期:  2019-12-03
  • 修回日期:  2020-06-08
  • 网络出版日期:  2020-07-11
  • 刊出日期:  2020-12-08

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