Routing and Spectrum Assignment Integrating Machine-Learning-Based QoT Estimation in Elastic Optical Networks

Machine Learning (ML) is under intense investigation in optical networks as it promises to lead to automation of a variety of management tasks, as amplifier gain equalization, fault recognition, Quality of Transmission (QoT) estimation, and many others. Though several studies focus on each of these specific tasks, the integration of ML-based estimations inside Routing and Spectrum Assignment (RSA) is still largely unexplored. This paper moves towards such integration. We develop a framework that leverages the probabilistic outputs of a ML-based QoT estimator to define the reach constraints in an Integer Linear Programming (ILP) formulation for RSA in an elastic optical network. In this integrated procedure, the RSA problem is solved iteratively by updating the reach constraints based on the outcome of a QoT estimator, to exclude lightpaths with unacceptable QoT. In our numerical evaluation, the proposed integrated method achieves savings in spectrum occupation up to 30% (around 20% on average) compared to traditional ILPbased RSA approaches with reach constraints based on margined analytical models.