Machine-learning-aided abstraction of photonic integrated circuits in software-defined optical transport

In order to cope with the fast increase in data traffic demand, optical networks are fast evolving towards the disaggregation and progressive implementation of the openness paradigm. Such an evolution is enabling the application of the software-defined networking below the IP layer, down to the optical transmission (SD-OTN). SD-OTN is enabled by the capability of the network controller to automatized management of photonic switching systems, and allowing their full virtualization and softwarisation. To this purpose, one of the major matter of contention is an efficient utilization of routing strategies, which can be seamlessly incorporated into the control plane. In this work, we rely on data-driven science (DDS) to develop the machine learning (ML) model which is able to predict the routing strategies of generic N x N photonic switching system without any knowledge required of the topology. The dataset used for training and testing the ML model is generated “synthetically”. In particular, the training and testing of the proposed ML module is done in a completely topological and technological agnostic way and is able to perform its application in real-time. Furthermore, the scalability and accuracy of the proposed approach is verified by considering two different switching topologies: the Honey-Comb Rearrangeable Optical Switch and the Benes network. Promising results are achieved in terms of predicting the control signals matrix for both of the considered topologies.