Machine Learning assisted aggregation schemes for optical cross-connect in hybrid electrical/optical data center networks

Abstract

Making optical circuit switching suitable for handling highly dynamic and profoundly changing traffic is a considerable challenge. This motivation drives the development of a hybrid electrical/optical network towards high bandwidth and low latency. Compared with the traditional non-aggregation scheme, we provide two machine learning assisted aggregation schemes. The first one is to design optical cross-connect switches to increase the throughput of the circuit-switched network. In this solution, the optical cross-connect serves both delay-sensitive traffic flows and delay-tolerant traffic flows. As the network throughput rises rapidly, the number of ports of the optical switch remains unchanged. The second scheme is to add small port counts, which maximizes throughput while relaxing the requirements for accurate machine learning algorithms. In this paper, we have a set of four machine learning algorithms, and only the most suitable one is selected at a time. We deploy a machine learning algorithm at edge nodes instead of a central network management system. Therefore, we can simultaneously reduce network overhead and latency. Both aggregation schemes outperform the traditional non-aggregation scheme in terms of throughput, delay, and flow completion time.