Routing is, arguably, the most fundamental task in computer networking, and the most extensively studied one. A key challenge for routing in real-world environments is the need to contend with uncertainty about future traffic demands. We present a new approach to routing under demand uncertainty: tackling this challenge as stochastic optimization, and employing deep learning to learn complex patterns in traffic demands. We show that our method provably converges to the global optimum in well-studied theoretical models of multicommodity flow. We exemplify the practical usefulness of our approach by zooming in on the real-world challenge of traffic engineering (TE) on wide-area networks (WANs). Our extensive empirical evaluation on real-world traffic and network topologies establishes that our approach's TE quality almost matches that of an (infeasible) omniscient oracle, outperforming previously proposed approaches, and also substantially lowers runtimes.
@article{arxiv.2303.00735,
title = {A Deep Learning Perspective on Network Routing},
author = {Yarin Perry and Felipe Vieira Frujeri and Chaim Hoch and Srikanth Kandula and Ishai Menache and Michael Schapira and Aviv Tamar},
journal= {arXiv preprint arXiv:2303.00735},
year = {2023}
}