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

Abstract

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.

Keywords

• software defined network;link failure;reinforcement learning;routing convergence