Teal: Learning-Accelerated Optimization of WAN Traffic Engineering
2023pp. 378–393
Citations Over TimeTop 1% of 2023 papers
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.
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