Reinforcement-learning-based path planning in multilayer elastic optical networks [Invited]

Abstract

This paper reports our study on the multilayer path (MLP) planning method in multilayer networks to achieve the flexible accommodation of large-capacity and diversified traffic. In addition to traffic grooming of sub-lambda paths, MLP design requires optimal selection of the operational mode. In this paper, we discuss MLP design methods that use reinforcement learning and auxiliary graphs to achieve MLP designs that satisfy various requirements such as cost, energy, and low blocking probability. We introduce a heuristic MLP planning method using auxiliary graphs. This method can determine link weights of auxiliary graphs to comply with arbitrary predefined policies; it yields MLPs whose characteristics satisfy the MLP requirements. We then describe an approach to optimize the weights of the auxiliary graph using reinforcement learning. In simulations, we evaluate the number of successfully allocated MLP paths, the required number of transceivers, and the capacity distribution of optical paths in a scenario where MLP requests are generated sequentially. The quantitative results show that our MLP design method can adaptively adjust the link weights of the auxiliary graphs under various network conditions. This can significantly improve the performance compared to heuristic design methods that assume fixed policies.