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A Deep Learning Perspective on Network Routing

Authors :
Perry, Yarin
Frujeri, Felipe Vieira
Hoch, Chaim
Kandula, Srikanth
Menache, Ishai
Schapira, Michael
Tamar, Aviv
Publication Year :
2023

Abstract

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.<br />Comment: To appear at NSDI 2023

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2303.00735
Document Type :
Working Paper