Learning to Flow (Between Datacenters)

Flow optimization is fundamental to computer science, and particularly to computer networks. A prominent example is flow optimization in the global “backbone networks” that interconnect service providers’ datacenters. We tackle a major gap between theory and practice: While in theoretical models upcoming traffic demands are typically known, in real-world networks such information is rarely available a priori. In practice, addressing this gap often involves predicting upcoming demands and optimizing for these. Using data from production networks, we show that this approach can produce solutions that deviate significantly from the optimum. We propose a novel approach: leveraging empirical data to directly learn flow configurations that deliver robustly high performance, bypassing the need for explicit demand prediction. We prove the optimality of our methodology. We further show that by building on recent advances in large-scale optimization and deep learning, our approach enables efficient training on extensive data, picking out intricate patterns in real-world traffic. Through extensive empirical evaluation, we demonstrate that our approach significantly outperforms the state of the art in terms of both solution quality and online runtimes. Leveraging ML models to directly learn network flow configurations from empirical data can deliver robustly high performance, surpassing that of demand-prediction methods.