Online Service Function Chain Deployment Method Based on Deep Q Network
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摘要: 针对5G网络资源状态动态变化和网络模型高维度下服务功能链部署的复杂性问题，该文提出一种基于深度Q网络的在线服务功能链部署方法(DeePSCD)。首先，为描述网络资源动态变化的特征，将服务功能链部署建模成马尔可夫决策过程，然后，针对系统资源模型的高维度问题采用深度Q网络的方法进行在线服务功能链部署策略求解。该方法可以有效描述网络资源状态的动态变化，特别是深度Q网络能有效克服求解复杂度，优化服务功能链的部署开销。仿真结果表明，所提方法在满足服务时延约束条件下降低了服务功能链的部署开销，提高了运营商网络的服务请求接受率。Abstract: To deal with the dynamic nature of 5G network resource state and the difficulty of service function chain deployment under the high-dimensional network state model, an online Service function Chain Deployment method based on Deep Q network (DeePSCD) is proposed. First, to describe the dynamic nature of network resource state, the service function chain deployment is modeled as a Markov decision process. Then, the deep Q network is used to solve the online service function chain deployment problem in the high-dimensional system resource model. This method can effectively describe the dynamic changes of network resource state. Specifically, deep Q network handles the complexity of problem and determines the optimal deployment solution of service function chain. Simulation results show that the proposed method can reduce the deployment cost of the service function chain and increase the acceptance rate while meeting the service delay constraint.
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表 1 算法1 基于DQN的在线服务链部署算法
输入：服务链信息${G_r} = \left( {N_r^{'},L_r^{'}} \right)$和当前网络状态${s_t}$ 输出：服务链部署策略$\pi _Q^*$ (1) 初始化经验复用池${{D} }$的容量${{M} }$ (2) 初始化动作对应的$Q$值为随机值 (3) 初始化选择策略$\pi $ (4) for episode in range(EPISODES): (5) 初始化状态$s$ (6) for step in range(STEPS): (7) 检查底层网络资源状态，生成满足条件服务器节点集合$\varPhi$ (8) 以$\varepsilon $的概率随机选择一个动作${a_t}$ (9) 否则选择${a_t} = {\max _a}{Q^*}\left( {{s_t},a;\theta } \right)$ (10) 在服务链部署模拟器中执行动作${a_t}$并观察对应的奖励
${r_t}$和下一个状态${s_{t + 1}}$(11) 在经验复用池中存储样本${e_t} = \left( {{s_t},{a_t},{r_t},{s_{t + 1}}} \right)$ (12) 从经验复用池中随机抽取小批量的样本$\left( {s_j},{a_j},{r_j},\right. $
$ \left.{s_{j + 1}} \right)$(13) 令${y_j} = \left\{ {\begin{array}{*{20}{c} } { {r_j} },& { {r_j} = {\rm{end} } } \\ { {r_j} + \gamma { {\max }_{a'} }Q\left( { {s_{j + 1} },a';\theta } \right)},& { {r_j} \ne {\rm{end} } } \end{array} } \right.$ (14) 在${\left( {{y_j} - Q\left( {{s_{j + 1}},a;\theta } \right)} \right)^2}$上执行梯度下降 (15) 每L步更新目标网络参数${\theta ^ - }$ (16) end (17) end (18) 根据${Q^*}\left( {{s_t},a;\theta } \right)$计算服务链部署方案${\pi ^*}$及其开销R 
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