Reconfiguring multicast sessions in elastic optical networks adaptively with graph-aware deep reinforcement learning

With the fast deployment of datacenters (DCs), bandwidth-intensive multicast services are becoming more and more popular in metro and wide-area networks, to support dynamic applications such as DC synchronization and backup. Hence, this work studies the problem of how to formulate and reconfigure multicast sessions in an elastic optical network (EON) dynamically. We propose a deep reinforcement learning (DRL) model based on graph neural networks to solve the sub-problem of multicast session selection in a more universal and adaptive manner. The DRL model abstracts topology information of the EON and the current provisioning scheme of a multicast session as graph-structured data, and analyzes the data to intelligently determine whether the session should be selected for reconfiguration. We evaluate our proposal with extensive simulations that consider different EON topologies, and the results confirm its effectiveness and universality. Specifically, the results show that it can balance the trade-off between the number of reconfiguration operations and blocking performance much better than existing algorithms, and the DRL model trained in one EON topology can easily adapt to solve the problem of dynamic multicast session reconfiguration in other topologies, without being redesigned or retrained. (C) 2021 Optical Society of America