Intelligent Link Load Control in a Segment Routing network via Deep Reinforcement Learning

In this paper we propose a framework based on Deep Reinforcement Learning to proactively and autonomously maintain the links load of a Segment Routing (SR) node under a pre-fixed threshold at milliseconds time scale. A local agent, powered by a Deep Q-Network (DQN) algorithm, referred to as intelligent Link Load Control (iLLC), selects are-routing operation to move traffic flows from overloaded links to alternative paths. The re-routing operation is performed in a transparent way for other network devices, without involving the centralized control plane, by exploiting the source routing feature of the SR. The performance evaluation conducted over real data sets shows that iLLC is able to distribute traffic load peaks o ver locallinks without degrading the overall network performance. Furthermore, iLLC outperforms a higher complexity heuristic based on capacity constraints checking, since it is able to select rerouting operations not impacting the global maximum link utilization.