Machine Learning-based Spectrum Resource Assignment and End-to-End Path Reconfiguration for Flexible Optical Networks

Optical resource management and path reconfiguration have become increasingly paramount in the era of dynamic and flexible optical networks. This paper presents machine learning-based optical resource assignment for highly efficient resource utilization in Spatial Division Multiplexing (SDM) networks. We also demonstrate a network orchestration with a Whitebox-based optical network and failure prediction in an SDM network.