semantic_scholarProceedings of the 10th Asia-Pacific Workshop on Networking2026-08-05
KaeTE: Towards Practical Neural Traffic Engineering with Lagrangian Duality and Learning-to-optimize
Zirui Ou, Yanghao Zhang, Jie Gui, Qun Huang
TL;DR: KaeTE is presented, an ML-based TE solver that supports dynamic network conditions and the throughput objective and adopts a learning-to-optimize (L2O)-inspired model to iteratively refine the dual variables and ensures that the final TE solutions do not overload any link.
Traffic engineering (TE) is becoming increasingly important in modern networks, as it can improve network performance by splitting traffic across paths. However, traditional TE solvers can be too slow for rapid changes, while recent machine learning (ML) solvers are fast but ofte…