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 often fail to support dynamic network conditions, such as topology changes or link capacity changes. Moreover, they usually support only simple TE objectives that do not account for potential link overload, which makes them less practical. In this paper, we present KaeTE, an ML-based TE solver that supports dynamic network conditions and the throughput objective. The design of KaeTE leverages the convexity of the throughput objective. Specifically, KaeTE first makes the throughput objective strongly convex through regularization. KaeTE then adopts a learning-to-optimize (L2O)-inspired model to iteratively refine the dual variables. To ensure that the final TE solutions do not overload any link, KaeTE generates the final TE solutions and the corresponding throughput objective value through a constraint-aware loss function. Evaluations on both dynamic and static network conditions show that KaeTE consistently outperforms baselines in our evaluated settings.