GATE: GPU-Accelerated Traffic Engineering for the WAN
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866911642830241792 |
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| author | Bothra, Rahul Krentsel, Alexander Mandal, Saptarshi Godfrey, Brighten Ratnasamy, Sylvia Shakir, Rob Srikant, R. |
| author_facet | Bothra, Rahul Krentsel, Alexander Mandal, Saptarshi Godfrey, Brighten Ratnasamy, Sylvia Shakir, Rob Srikant, R. |
| contents | Traffic engineering (TE) has become a crucial tool for enforcing routing policy and maintaining operational efficiency in large networks. Existing TE solutions pick an objective function to optimize, aiming to balance (i) allocating traffic optimally with (ii) reacting quickly to demand changes and disruption events. However, as the scale of networks grows, the runtime of the existing optimal solution becomes infeasibly large. The alternative - approximate solvers - result in costly inefficiencies.
We present GPU-Accelerated Traffic Engineering (GATE), which achieves the best of both worlds: enabling fast TE runtimes through a highly-parallelizable GPU-compatible decomposition, while iteratively converging to the provably optimal solution. GATE unlocks a unique set of desirable properties: it becomes increasingly parallelizable with network size, supports a wide spectrum of fairness objectives, and offers theoretically guaranteed convergence to the optimal solution and near-optimal convergence within a bounded time. We evaluate GATE on production traces from two large cloud WANs, and show that GATE achieves near-optimal solutions 5-10x faster than state-of-the-art. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_01748 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GATE: GPU-Accelerated Traffic Engineering for the WAN Bothra, Rahul Krentsel, Alexander Mandal, Saptarshi Godfrey, Brighten Ratnasamy, Sylvia Shakir, Rob Srikant, R. Networking and Internet Architecture Traffic engineering (TE) has become a crucial tool for enforcing routing policy and maintaining operational efficiency in large networks. Existing TE solutions pick an objective function to optimize, aiming to balance (i) allocating traffic optimally with (ii) reacting quickly to demand changes and disruption events. However, as the scale of networks grows, the runtime of the existing optimal solution becomes infeasibly large. The alternative - approximate solvers - result in costly inefficiencies. We present GPU-Accelerated Traffic Engineering (GATE), which achieves the best of both worlds: enabling fast TE runtimes through a highly-parallelizable GPU-compatible decomposition, while iteratively converging to the provably optimal solution. GATE unlocks a unique set of desirable properties: it becomes increasingly parallelizable with network size, supports a wide spectrum of fairness objectives, and offers theoretically guaranteed convergence to the optimal solution and near-optimal convergence within a bounded time. We evaluate GATE on production traces from two large cloud WANs, and show that GATE achieves near-optimal solutions 5-10x faster than state-of-the-art. |
| title | GATE: GPU-Accelerated Traffic Engineering for the WAN |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2605.01748 |