Automatic Management of N × N Photonic Switch Powered by Machine Learning in Software-Defined Optical Transport

Optical networking is fast evolving towards the applications of the Software-defined Networking (SDN) paradigm down to the (Wavelength-division Multiplexing) WDM transport layer for cost-effective and flexible infrastructure management. Optical SDN requires each network element’s software abstraction to enable full control by the centralized network controller. Nowadays, modern network elements, especially photonic switching systems, are developed by exploiting the fast-emerging technology of Photonic Integrated Circuit (PIC) that consists of complex fabrics of elementary units that can be driven individually using a large set of elementary controls. In this work, we focus on modeling the elementary control states of the topological structures behind PIC ${N} \times {N}$ switches under a fully blind approach based on Machine Learning (ML) techniques. The ML agent’s training and testing datasets are obtained synthetically by software simulation of the photonic switch structure. The proposed technique’s scalability and accuracy are validated by considering different dimensions ${N}$ and applying it to two different switching topologies: the Honey-Comb Rearrangeable Optical Switch and the Benes network. Excellent results in terms of prediction of the control states are achieved for both of the considered topologies.