DOTE: Rethinking (Predictive) WAN Traffic Engineering


Yarin Perry, Hebrew University of Jerusalem; Felipe Vieira Frujeri, Microsoft Research; Chaim Hoch, Hebrew University of Jerusalem; Srikanth Kandula and Ishai Menache, Microsoft Research; Michael Schapira, Hebrew University of Jerusalem; Aviv Tamar, Technion
Awarded Best Paper!


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.

NSDI '23 Open Access Sponsored by
King Abdullah University of Science and Technology (KAUST)

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@inproceedings {286421,
author = {Yarin Perry and Felipe Vieira Frujeri and Chaim Hoch and Srikanth Kandula and Ishai Menache and Michael Schapira and Aviv Tamar},
title = {{DOTE}: Rethinking (Predictive) {WAN} Traffic Engineering},
booktitle = {20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23)},
year = {2023},
isbn = {978-1-939133-33-5},
address = {Boston, MA},
pages = {1557--1581},
url = {},
publisher = {USENIX Association},
month = apr,

Presentation Video