QL-STCT：一种SDN链路故障智能路由收敛方法

针对软件定义网络（SDN）链路故障发生时的路由收敛问题，提出了Q-Learning子拓扑收敛技术（QL-STCT）实现软件定义网络链路故障时的路由智能收敛。首先，选取网络中的部分节点作为枢纽节点，依据枢纽节点进行枢纽域的划分。然后，以枢纽域为单位构建区域特征，利用特征提出强化学习智能体探索策略来加快强化学习收敛。最后，通过强化学习构建子拓扑网络用于规划备用路径，并保证在周期窗口内备用路径的性能。实验仿真结果表明，所提方法能够有效提高链路故障网络的收敛速度与性能。

QL-STCT: an intelligent routing convergence method for SDN link failure

Aiming at the problem of routing convergence when SDN link failure occurs, a Q-Learning sub-topological convergence technique(QL-STCT) was proposed to realize intelligent route convergence when SDN links fail. Firstly,some nodes were selected in the network as hub nodes and divides the hub domains according to the hub nodes, and the regional features were constructed with the hub domain as the unit. Secondly, the reinforcement learning agent exploration strategy was proposed by using the features to accelerate the convergence of reinforcement learning. Finally, a sub-topology network was constructed through reinforcement learning to plan the alternate path and ensure the performance of the alternate path in the periodic window. Experimental simulation results show that the proposed method effectively improves the convergence speed and performance of the link failure network.