Optimized Deployment Method of Edge Computing Network Service Function Chain Delay Combined with Deep Reinforcement Learning
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摘要: 该文针对边缘网络资源受限且对业务流端到端时延容忍度低的问题，结合深度强化学习与基于时延的Dijkstra寻路算法提出一种面向时延优化的服务功能链(SFC)部署方法。首先，设计一种基于注意力机制的序列到序列(Seq2Seq)代理网络和基于时延的Dijkstra寻路算法，用于产生虚拟网络功能(VNF)的部署以及服务SFC的链路映射，同时考虑了时延优化模型的约束问题，采用拉格朗日松弛技术将其纳入强化学习目标函数中；其次，为了辅助网络代理快速收敛，采用基线评估器网络评估部署策略的预期奖励值；最后，在测试阶段，通过贪婪搜索及抽样技术降低网络收敛到局部最优的概率，从而改进模型的部署。对比实验表明，该方法在网络资源受限的情况下，比First-Fit算法与TabuSearch算法的时延分别降低了约10%和86.3%，且较这两种算法稳定约74.2%与84.4%。该方法能较稳定地提供更低时延的端到端服务，使时延敏感类业务获得更好体验。Abstract: A delay-optimized Service Function Chain (SFC) deployment approach is proposed by combining deep reinforcement learning with the delay-based Dijkstra pathfinding algorithm for the problem of resource-constrained edge networks and low end-to-end delay tolerance for service flows. Firstly, an attention mechanism-based Sequence to Sequence (Seq2Seq) agent network and a delay-based Dijkstra pathfinding algorithm are designed for generating Virtual Network Function(VNF) deployments and link mapping for SFC, while the constraint problem of the delay optimization model is considered and incorporated into the reinforcement learning objective function using Lagrangian relaxation techniques; Secondly, to assist the network agent in converging quickly, a baseline evaluator network is used to assess the expected reward value of the deployment strategy; Finally, in the testing phase, the deployment strategy of the agent is improved by reducing the probability of convergence of the network to a local optimum through greedy search and sampling techniques. Comparison experiments show that the method reduces the latency by about 10% and 86.3% than the First-Fit algorithm and TabuSearch algorithm, respectively, and is about 74.2% and 84.4% more stable than these two algorithms in the case of limited network resources. This method provides a more stable end-to-end service with lower latency, enabling a better experience for latency-sensitive services.
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算法1 结合基于时延的Dijkstra与DRL算法的训练过程 输入：网络服务请求${s_i}$和网络状态信息；惩罚权重${\alpha _{{\rm{occupancy}}}}$, ${\alpha _{{\rm{bandwidth}}}}$；环境时延的相关参数； epoch, batch； 输出：SFC的部署策略${p^{{s_i}}}$，以及业务请求的端到端时延预估值 DDRL训练过程： (1) 初始化参数$\theta $, ${\theta _v}$；$p \leftarrow \varnothing $, $\varOmega \leftarrow 0$, ${l_i} \leftarrow \varnothing $ , ${s_i} \leftarrow \varnothing $, $L\left( {p|s} \right) \leftarrow 0$和循环次数epoch (2) for epoch = 1, 2 ,···, do (3) 重置梯度${\mathrm{d}}\theta \leftarrow 0$ (4) 生成batch个SFC实例$S$；根据式(11)计算的概率为VNF${f_i}$选择部署位置${v_i} \in {V^P}$ (5) 由基于时延的Dijkstra寻路算法为各VNF${f_i}$找到时延最小的路径树${l_i}$ (6) ${s_j}$～对输入$S$进行采样，$j \in \left\{ {1,2,\cdots,B} \right\}$；${p_j}$对相应策略${\pi _\theta }$ 进行抽样，$j \in \left\{ {1,2,\cdots,B} \right\}$ (7) 采样得到的${s_j}$通过基线估计器预估奖励${b_j} \leftarrow {b_{{\theta _v}}}\left( {{s_j}} \right)$,$j \in \left\{ {1,2,\cdots,B} \right\}$ (8) 计算真实奖励$G\left( {{p_j}} \right)$、根据式(11)计算近似梯度${\nabla _\theta }J_G^\pi \left( \theta \right)$ (9) 根据式(15)计算预测奖励值${b_j}$与真实奖励值$G\left( {{p_j}} \right)$均方误差${\nabla _{{\theta _v}}}J_G^\pi \left( {{\theta _v}} \right)$ (10) $\theta \leftarrow {\mathrm{Adam}}\left( {\theta ,{\nabla _\theta }J_G^\pi \left( \theta \right)} \right)$, ${\theta _v} \leftarrow {\mathrm{Adam}}\left( {{\theta _v},{\nabla _{{\theta _v}}}J_G^\pi \left( {{\theta _v}} \right)} \right)$ (11) end for (12) return$\theta $,${\theta _v}$, $L\left( {p|{s_j}} \right)$, $\zeta \left( {p|{s_j}} \right)$ (13) 训练结束 
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算法2 结合基于时延的Dijkstra与DRL算法的SFC部署方法 输入：网络服务请求${s_i}$和网络状态信息；惩罚权重${\alpha _{{\rm{occupancy}}}}$, ${\alpha _{{\rm{bandwidth}}}}$；环境时延的相关参数 输出：SFC的部署策略${p^{{s_i}}}$ DDRL网络服务请求部署过程： (1) 初始化放置序列$p \leftarrow \varnothing $，时延奖励$L\left( {p|s} \right) \leftarrow \varnothing $，惩罚值$\zeta \left( {p|s} \right) \leftarrow \varnothing $，拉格朗日值$G\left( {p|s} \right) \leftarrow \varnothing $以及2个约束${\alpha _{{\mathrm{occupancy}}}} \leftarrow \varnothing $,
${\alpha _{{\rm{bandwidth}}}} \leftarrow \varnothing $(2) 遍历每个网络模型for m in range(models)： (3) 加载模型m的参数$ \theta $,${\theta _v}$ (4) for batch in range(128): (5) 生成第batch条SFC的贪婪放置序列$p$, ${\text{l\_m}}[{\text{batch}}] \leftarrow G\left( {p|s} \right)$ (6) for sample in range(${\text{temp\_sample}}$): (7) 通过温度超参数$T$控制输出分布的稀疏性，生成稀疏的放置序列${\text{p\_temp}}$ (8) ${\text{l\_m\_temp[sample][batch]}} \leftarrow G({\text{p\_temp}}|s)$ (9) 取$G({\text{p\_temp}}|s)$最小的放置序列作为第m个网络模型的温度抽样放置序列${\text{p\_m\_temp}}$ (10) 生成贪婪放置${\text{p\_m}}$对应的拉格朗日值序列${\text{l\_m}}$, ${\text{l\_m\_temp}}$与惩罚值序列${\text{penalty\_m}}$, ${\text{penalty\_m\_temp}}$ (11) 取两种搜索算法的最小惩罚值，对应的放置序列为最优放置决策$p$ (12) end for (13) return ${\mathrm{placement}}$, $L\left( {p|s} \right)$,$\zeta \left( {p|s} \right)$ (14) 部署结束
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