Learning-Augmented Online Optical Interconnect Reconfiguration

Optical interconnects are a novel switching technology currently deployed in data center networks due to their extremely low latency and high bandwidth. However, dynamically establishing optical circuits is not free and introduces undesirable delays, disrupting the network during reconfigurations. Existing approaches focus on two ends of a spectrum: either they dynamically reconfigure the network in an oblivious manner, or they optimize for long-lasting static solutions. We study the online optimization problem of such interconnects and present a learning-augmented algorithm that decides when and how to reconfigure. Our algorithm leverages predictions of the incoming traffic to strike a smooth tradeoff between the two previous extremes and shows close-to-optimal performances with regards to the predictions' accuracy.