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Teal: Learning-Accelerated Optimization of WAN Traffic Engineering

Authors:

Francis Y. Yan,

Justin T. Chiu,

Alexander M. Rush,

Minlan YuAuthors Info & Claims

ACM SIGCOMM '23: Proceedings of the ACM SIGCOMM 2023 Conference

Pages 378 - 393

https://doi.org/10.1145/3603269.3604857

Published: 01 September 2023 Publication History

Abstract

The rapid expansion of global cloud wide-area networks (WANs) has posed a challenge for commercial optimization engines to efficiently solve network traffic engineering (TE) problems at scale. Existing acceleration strategies decompose TE optimization into concurrent subproblems but realize limited parallelism due to an inherent tradeoff between run time and allocation performance.

We present Teal, a learning-based TE algorithm that leverages the parallel processing power of GPUs to accelerate TE control. First, Teal designs a flow-centric graph neural network (GNN) to capture WAN connectivity and network flows, learning flow features as inputs to downstream allocation. Second, to reduce the problem scale and make learning tractable, Teal employs a multi-agent reinforcement learning (RL) algorithm to independently allocate each traffic demand while optimizing a central TE objective. Finally, Teal fine-tunes allocations with ADMM (Alternating Direction Method of Multipliers), a highly parallelizable optimization algorithm for reducing constraint violations such as overutilized links.

We evaluate Teal using traffic matrices from Microsoft's WAN. On a large WAN topology with >1,700 nodes, Teal generates near-optimal flow allocations while running several orders of magnitude faster than the production optimization engine. Compared with other TE acceleration schemes, Teal satisfies 6--32% more traffic demand and yields 197--625× speedups.

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Cited By

Liu HHuang SQin SYang TYang TXiang QLiu X(2024)Keep Your Paths Free: Toward Scalable Learning-Based Traffic EngineeringProceedings of the 8th Asia-Pacific Workshop on Networking10.1145/3663408.3665813(189-191)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.1145/3663408.3665813
Liu XZhao SCui YWang XSekar VYu MSeneviratne AVeitch D(2024)FIGRET: Fine-Grained Robustness-Enhanced Traffic EngineeringProceedings of the ACM SIGCOMM 2024 Conference10.1145/3651890.3672258(117-135)Online publication date: 4-Aug-2024
https://dl.acm.org/doi/10.1145/3651890.3672258
Miao CZhong ZXiao YYang FZhang SJiang YBai ZLu CGeng JHe ZWang YZou XYang CSekar VYu MSeneviratne AVeitch D(2024)MegaTE: Extending WAN Traffic Engineering to Millions of Endpoints in Virtualized CloudProceedings of the ACM SIGCOMM 2024 Conference10.1145/3651890.3672242(103-116)Online publication date: 4-Aug-2024
https://dl.acm.org/doi/10.1145/3651890.3672242
Show More Cited By

Index Terms

Teal: Learning-Accelerated Optimization of WAN Traffic Engineering
1. Computing methodologies
  1. Machine learning
2. Networks
  1. Network algorithms
    1. Control path algorithms
      1. Traffic engineering algorithms

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cover image ACM Conferences

ACM SIGCOMM '23: Proceedings of the ACM SIGCOMM 2023 Conference

September 2023

1217 pages

ISBN:9798400702365

DOI:10.1145/3603269

Chairs:
Henning Schulzrinne,
Vishal Misra,
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Eddie Kohler,
David Maltz

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Published: 01 September 2023

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Cited By

Liu HHuang SQin SYang TYang TXiang QLiu X(2024)Keep Your Paths Free: Toward Scalable Learning-Based Traffic EngineeringProceedings of the 8th Asia-Pacific Workshop on Networking10.1145/3663408.3665813(189-191)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.1145/3663408.3665813
Liu XZhao SCui YWang XSekar VYu MSeneviratne AVeitch D(2024)FIGRET: Fine-Grained Robustness-Enhanced Traffic EngineeringProceedings of the ACM SIGCOMM 2024 Conference10.1145/3651890.3672258(117-135)Online publication date: 4-Aug-2024
https://dl.acm.org/doi/10.1145/3651890.3672258
Miao CZhong ZXiao YYang FZhang SJiang YBai ZLu CGeng JHe ZWang YZou XYang CSekar VYu MSeneviratne AVeitch D(2024)MegaTE: Extending WAN Traffic Engineering to Millions of Endpoints in Virtualized CloudProceedings of the ACM SIGCOMM 2024 Conference10.1145/3651890.3672242(103-116)Online publication date: 4-Aug-2024
https://dl.acm.org/doi/10.1145/3651890.3672242
AlQiam AYao YWang ZAhuja SZhang YRao SRibeiro BTawarmalani MSekar VYu MSeneviratne AVeitch D(2024)Transferable Neural WAN TE for Changing TopologiesProceedings of the ACM SIGCOMM 2024 Conference10.1145/3651890.3672237(86-102)Online publication date: 4-Aug-2024
https://dl.acm.org/doi/10.1145/3651890.3672237
Gui FWang SLi DChen LGao KMin CWang YSekar VYu MSeneviratne AVeitch D(2024)RedTE: Mitigating Subsecond Traffic Bursts with Real-time and Distributed Traffic EngineeringProceedings of the ACM SIGCOMM 2024 Conference10.1145/3651890.3672231(71-85)Online publication date: 4-Aug-2024
https://dl.acm.org/doi/10.1145/3651890.3672231
Wu HYan JKuang L(2024)Asynchronous Multi-Class Traffic Management in Wide Area NetworksIEEE Transactions on Network and Service Management10.1109/TNSM.2024.335479321:2(1750-1763)Online publication date: Apr-2024
https://doi.org/10.1109/TNSM.2024.3354793
Nance-Hall MBarford PFoerster KDurairajan R(2024)Improving Scalability in Traffic Engineering via Optical Topology ProgrammingIEEE Transactions on Network and Service Management10.1109/TNSM.2023.333589821:2(1581-1600)Online publication date: Apr-2024
https://doi.org/10.1109/TNSM.2023.3335898
Nougnanke BLoye JBaffier JFerlin SBruyere MLabit Y(2024)gPerfIsol: GNN-Based Rate-Limits Allocation for Performance Isolation in Multi-Tenant Cloud2024 27th Conference on Innovation in Clouds, Internet and Networks (ICIN)10.1109/ICIN60470.2024.10494419(194-201)Online publication date: 11-Mar-2024
https://doi.org/10.1109/ICIN60470.2024.10494419
Gu CSong XNg BXiang QGuo ZLi G(2024)An ML-Accelerated Framework for Large-Scale Constrained Traffic Engineering2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS)10.1109/ICDCS60910.2024.00014(47-58)Online publication date: 23-Jul-2024
https://doi.org/10.1109/ICDCS60910.2024.00014
Le VLong Nguyen DLe Nguyen PJi Y(2024)Traffic Engineering in Large-scale Networks via Multi-Agent Deep Reinforcement Learning with Joint-Training2024 33rd International Conference on Computer Communications and Networks (ICCCN)10.1109/ICCCN61486.2024.10637556(1-9)Online publication date: 29-Jul-2024
https://doi.org/10.1109/ICCCN61486.2024.10637556
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