基于强化学习的高可靠性多域虚拟网络映射算法

High-reliability multi-domain virtual network mapping algorithm based on reinforcement learning

现有的虚拟网络映射算法大多是依赖于人工规则对节点进行排序,决定节点先后映射的顺序,来优化节点映射从而提高虚拟网络请求的成功率。而在链路映射阶段普遍采用广度优先搜索算法,忽略了节点资源和链路资源具有强相关性的特点,从而只能取得局部最优的映射结果。针对上述问题,基于5G多域异构网络环境,从网络的可生存性的保护角度出发,提出一种使用双层强化学习的虚拟网络映射算法。将强化学习同时应用于网络映射的节点和链路两阶段,使用梯度策略和反向传播的方法对该网络模型进行训练,并使用此训练模型完成映射。仿真结果表明,与对比算法相比,该算法在优化节点映射的同时优化了链路映射,且在映射成功率、长期收益率、节点和链路的利用率等方面均取得较好结果。

Most of the existing virtual network mapping algorithms rely on manual rules to sort nodes and determine the sequence of node mapping so as to optimize node mapping and improve the success rate of virtual network requests.In the link mapping stage,it generally uses the breadth-first search algorithm,ignoring the strong correlation between node resources and link resources,so that it can only obtain local optimal mapping results.In response to the above problems,based on the 5G multi-domain heterogeneous network environment,from the perspective of network survivability protection,this paper proposed a virtual network mapping algorithm using two-layer reinforcement learning.It applied reinforcement learning to both the node and link stages of network mapping,used the gradient strategy and back propagation method to train the network model of this paper,and used the training model of this paper to complete the mapping.The simulation results show that,compared with the comparison algorithms,the algorithm optimizes the link mapping while optimizing the node mapping,and achieves better results in the mapping success rate,long-term return rate,and node and link utilization rate.