AI RESEARCH
Geminet: Learning the Duality-based Iterative Process for Lightweight Traffic Engineering in Changing Topologies
arXiv CS.LG
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ArXi:2506.23640v2 Announce Type: replace-cross Recently, researchers have explored ML-based Traffic Engineering (TE), leveraging neural networks to solve TE problems traditionally addressed by optimization. However, existing ML-based TE schemes remain impractical: they either fail to handle topology changes or suffer from poor scalability due to excessive computational and memory overhead. To overcome these limitations, we propose Geminet, a lightweight and scalable ML-based TE framework that can handle changing topologies.