DOTE: Rethinking (Predictive) WAN Traffic Engineering

Yarin Perry, Felipe Vieira Frujeri, Chaim Hoch, Srikanth Kandula, Ishai Menache, Michael Schapira, Aviv Tamar

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

5 Scopus citations

Abstract

We explore a new design point for traffic engineering on wide-area networks (WANs): directly optimizing traffic flow on the WAN using only historical data about traffic demands. Doing so obviates the need to explicitly estimate, or predict, future demands. Our method, which utilizes stochastic optimization, provably converges to the global optimum in well-studied theoretical models. We employ deep learning to scale to large WANs and real-world traffic. 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.

Original languageAmerican English
Title of host publicationProceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023
PublisherUSENIX Association
Pages1557-1581
Number of pages25
ISBN (Electronic)9781939133335
StatePublished - 2023
Event20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023 - Boston, United States
Duration: 17 Apr 202319 Apr 2023

Publication series

NameProceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023

Conference

Conference20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023
Country/TerritoryUnited States
CityBoston
Period17/04/2319/04/23

Bibliographical note

Publisher Copyright:
© NSDI 2023.All rights reserved

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