Toward Scalable and High-Performance GNN-Based Traffic Engineering with Free Path Selection
Su, Qiang Jiang, Yining Huang, Siyong Song, Qingyu Xiang, Qiao Liu, Xue Shu, Jiwu
Su, Qiang
Jiang, Yining
Huang, Siyong
Song, Qingyu
Xiang, Qiao
Liu, Xue
Shu, Jiwu
Supervisor
Department
Machine Learning
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Conference proceeding
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Abstract
Traffic engineering (TE) is widely used to optimize network performance in modern networks. Typically, TE is formulated as a multiple-commodity flow (MCF) optimization problem and solved using mathematical solvers or machine learning approaches, but it becomes unscalable as the network size grows. Existing methods often limit available paths for flow allocation to speed up problem-solving, but this compromises TE performance. Achieving both high performance and fast decisionmaking with free path selection remains a significant challenge. This paper proposes TELD, a scalable and high-performance TE framework with free path selection. TELD leverages Graph Neural Networks (GNNs) that are widely proven with high efficiency in capturing network-specific characteristics and enabling faster decision-making than mathematical solvers. Our key idea is to reformulate the MCF problem into a learningfriendly representation and integrate TE constraints directly into GNN training and inference. The key challenge here is how to efficiently combine the problem reformulation with GNN. TELD tackles this with two critical designs. First, observing that GNNs work better with continuous features, TELD relaxes the freepath MCF formulation by treating flow allocation variables as continuous rather than discrete. Second, TELD introduces a multi-constraint hybrid GNN and a result fine-tuning mechanism to further improve GNN efficiency in TE. Extensive experiments show that TELD outperforms the state-of-the-art GNN-based TE framework by $\sim 55\%$ and reduces decision latency by three orders of magnitude compared to mathematical solvers.
Citation
Q. Su, Y. Jiang, S. Huang, Q. Song, Q. Xiang, X. Liu , et al., "Toward Scalable and High-Performance GNN-Based Traffic Engineering with Free Path Selection," 2026, pp. 1-10.
Source
https://ieeexplore.ieee.org/document/11322910
Conference
IEEE 31th International Conference on Parallel and Distributed Systems (ICPADS)
Keywords
40 Engineering, 4005 Civil Engineering, 7 Affordable and Clean Energy
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Source
IEEE 31th International Conference on Parallel and Distributed Systems (ICPADS)
Publisher
IEEE