TY - GEN
T1 - DOTE
T2 - 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023
AU - Perry, Yarin
AU - Frujeri, Felipe Vieira
AU - Hoch, Chaim
AU - Kandula, Srikanth
AU - Menache, Ishai
AU - Schapira, Michael
AU - Tamar, Aviv
N1 - Funding Information: Acknowledgements: We thank our shepherd, Mojgan Ghasemi, and the NSDI reviewers, for their valuable feedback. We thank Umesh Krishnaswamy, Himanshu Raj and the SWAN team at Microsoft for their help and feedback. Yarin Perry and Michael Schapira were partially supported by BSF grant 2019798 and a grant from Microsoft. Aviv Tamar is funded by ERC grant 101041250. Publisher Copyright: © NSDI 2023.All rights reserved
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85159362842
M3 - Conference contribution
T3 - Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023
SP - 1557
EP - 1581
BT - Proceedings of the 20th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2023
Y2 - 17 April 2023 through 19 April 2023
ER -