LMTE: Putting the "Reasoning" into WAN Traffic Engineering with Language Models

TL;DR

This paper introduces LMTE, a novel framework leveraging large language models for WAN traffic engineering, demonstrating superior performance, robustness, and speed compared to traditional methods.

Contribution

It is the first to explore large language models for traffic engineering, showing their ability to simulate decision processes and perform parallel reasoning.

Findings

Achieves up to 15% better maximum link utilization.

Demonstrates robustness with less than 5% performance degradation under dynamic conditions.

Provides 10 to 100 times speedup over traditional solvers.

Abstract

The rapid expansion of modern wide-area networks (WANs) has made traffic engineering (TE) increasingly challenging, as traditional solvers struggle to keep pace. Although existing offline ML-driven approaches accelerate TE optimization with deep neural networks (DNNs), they often lack sufficient expressiveness and generalization on unseen traffic patterns or topologies, limiting their practicality. Inspired by the success of large language models (LMs), for the first time, this paper investigates their potential as general-purpose traffic planners. Our contributions are two-fold: (i) Theoretically, we show that pre-trained LMs can simulate the sequential decision processes underlying TE and, crucially, exhibit parallel reasoning capabilities, making them well-suited for the task; (ii) Practically, we present LMTE, a novel LM-driven TE framework that embraces these insights through efficient multimodal alignment and lightweight configuration generation, all while preserving the model's original abilities. Extensive experiments demonstrate that fold matches top-tier performance on five datasets, achieving up to 15\% better maximum link utilization (MLU) and consistently lower performance degradation across diverse scenarios, e.g., less than 5\% with high traffic dynamics and link failures. Moreover, it achieves 10 to 100 times speedups over traditional TE solvers. To aid future works, our codebase is available at https://github.com/Y-debug-sys/LMTE.