Learning for Accelerated Traffic Engineering With Differentiable Network Modeling

Traffic engineering (TE) in the wide-area networks (WAN) is crucial for optimizing network performance by distributing traffic across various paths. Traditional methods use optimization like linear programs (LP) to solve TE, resulting in long decision-making time especially in larger topologies due to the iterative nature of optimization algorithms. Deep neural networks (DNNs) have been recently applied to TE to expedite decision-making through model inference. However, existing works rely on discrete event-based network simulators or non-differentiable algorithms to compute TE metrics from decisions by interacting with the network environment, breaking the gradient chains from decisions to metrics. Thus, TE metrics are treated as scalar values without gradients, limiting the learning paradigm for TE to reinforcement learning (RL) only. This paper demonstrates that calculating TE metrics can be fully differentiable, enabling direct gradient-based DNN updates for better TE decision-making, surpassing RL's reliance on approximated value functions. We propose dNE, a lightweight network simulator that uses differentiable matrix operations to evaluate TE decisions and compute user-defined metrics, enabling advanced DNN training paradigms like goal-driven optimization supervised by TE metric gradients. With dNE, experiments on four DNN-based TE algorithms show that DNNs trained with metric gradients reduce performance loss by over 10x compared to RL, speed up LP solvers by 13000x, and achieve 1000x faster metric computation than traditional event-based simulators.