Monte Carlo Tree Search for Cross-Stratum Optimization of Survivable Inter-Data Center Elastic Optical Network

In last few years, cloud computing and other services based on data centers have evolved from an emerging technology to a recognized approach that is gaining broad acceptance and deployment. Therefore, there is a significant need to provide efficient and reliable operation of inter-data center networks based on optical technologies. In this article, we focus on cross stratum optimization of an inter-data center elastic optical network with additional survivability requirements. We propose a novel optimization approach that employs machine learning Monte Carlo Tree Search (MCTS) algorithm for simulation of future traffic to improve the performance of the network regarding blocking probability and operational cost. We evaluate the performance of the proposed method and other reference algorithms under various network scenarios, using representative topologies and real data provided by Amazon Web Services. The main conclusion is that the approach based on the MCTS algorithm enables better coordination of resource allocation in both strata, which results in lower blocking of requests and lower cost taking into account the survivability requirements, in comparison to other algorithms.