DRL assisted multi-objective algorithm for multicast scheduling in elastic optical network

Multicasting, as a main transmission mode for most of the current applications in elastic optical network, has attracted more and more research attention. In this paper, we investigate multicast scheduling model and solving algorithm. First, we model the multicast scheduling problem as a multi-objective optimization problem (MOP) by minimizing the bandwidth resources and maximizing the user service quality, and then, we propose a deep reinforcement learning assisted multi-objective algorithm for the model (DRL-MM), in which we design source node selection strategy, routing scheme, modulation scheme and spectrum assignment scheme for each multicast session. To identify the superiority of the proposed DRL-MM, we conduct the experiments and compare DRL-MM with an approximation based Steiner tree algorithm (STA-RSA) and a load-balancing routing tree-based algorithm (LD-RSA) through the experiments. The results show that DRL-MM outperforms STA-RSA and LD-RSA in terms of both bandwidth resource usage and user service quality.