Learning-Based Adaptive Range Routing for Traffic Engineering With Graph Neural Networks

Traffic Engineering (TE) has been widely used by network operators to improve network performance and deliver better service quality. One major challenge for TE is providing routing strategies that can adapt to highly dynamic future traffic scenarios. Unfortunately, existing works either suffer severe performance degradation under unexpected traffic fluctuations, or sacrifice optimality to guarantee worst-case performance when traffic remains relatively stable. In this paper, we propose LARRI, a learning-based TE framework that predicts adaptive routing strategies for unknown future traffic scenarios. By integrating future demand range prediction and optimal range routing imitation into a single step, LARRI learns to generate a routing strategy that accommodates a wide range of possible future traffic matrices, thereby achieving a good trade-off between performance optimality and worst-case guarantees. Moreover, LARRI employs a scalable graph neural network architecture, which greatly facilitates both training and inference. Extensive simulations on six real-world network topologies show that LARRI achieves near-optimal load balancing in future traffic scenarios, improves worst-case performance by up to 43.3% over state-of-the-art baselines, and consistently provides the lowest end-to-end delay under dynamic traffic fluctuations.