Experimental evaluation of a latency-aware routing and spectrum assignment mechanism based on deep reinforcement learning

The introduction of futuristic and challenging use cases of 5G and 6G communications will demand strict requirements in terms of high bandwidth and low latency. Optical backbone networks need to tackle these new network scenarios by offering highly efficient, flexible, and scalable technologies and solutions. In this context, elastic optical networks (EONs) have been recognized as a promising technological transport infrastructure for the future Internet since they can manage the optical spectrum with enhanced flexibility and efficiency. The service provisioning in EONs is a challenging issue to tackle since the routing and spectrum assignment (RSA) is characterized by a high degree of complexity. This work presents an approach for RSA in EONs leveraging the advantages of deep reinforcement learning (DRL) solutions. The devised approach jointly considers the constraints imposed by the optical technologies and the demanded connectivity service requirements (i.e., guaranteed bandwidth and maximum end-to-end latency) when computing and selecting the optical path and spectral resources. We first evaluate our approach through simulation experiments considering two reference network topologies, demonstrating its effectiveness in reducing the bandwidth blocking ratio, the path computation time, and the number of rejected connectivity services requiring lower latencies when compared to a baseline k-shortest path routing and first-fit spectrum allocation algorithm. Then, the trained DRL agent is integrated within a real proof of concept to attain an ML-assisted SDN control plane in the CTTC ADRENALINE testbed. The attained performance improvements highlight the potential benefits brought by using DRL mechanisms and its feasible integration within production EON transport infrastructures.