Reliable Deployment Algorithm of Service Function Chain Based on Deep Reinforcement Learning
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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。
Abstract:In view of the current deployment of the Service Function Chain (SFC), the failure importance of the Virtual Network Function (VNF) is not considered,an SFC reliable deployment algorithm based on deep reinforcement learning is proposed. Firstly, a reliable mapping model of VNF and virtual links is establised, high reliability requirements is set for important VNFs, and the reliability requirements of virtual links is ensured as much as possible through link deployment length restrictions. Secondly, taking load balancing as the resource coordination principle, joint optimization the VNF reliability is jointly optimized. Finally, the deep reinforcement learning is used to get the service function chain deployment strategy. In addition, node backup and link backup strategies based on importance are proposed to deal with situations where VNF/link reliability is difficult to meet during deployment. Simulation results show that the reliable deployment algorithm in this paper can effectively reduce the failure SFC loss on the basis of ensuring the reliability requirements, and at the same time make the virtual network more stable and reliable.
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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 
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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。endifelse：$g'(v_I^k)$←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。endifuntil $RN(g'(v_I^k)) > {(R_k^{{\rm{req}}})^{{w_I}}}$or 已遍历所有组合 return 节点部署方案$g'(v_I^k)$
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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
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