Traffic Engineering (TE) in large-scale networks like cloud Wide Area Networks (WANs) and Low Earth Orbit (LEO) satellite constellations is a critical challenge. Although learning-based approaches have been proposed to address the scalability of traditional TE algorithms, their practical application is often hindered by a lack of generalization, high training overhead, and a failure to respect link capacities. This paper proposes TELGEN, a novel TE algorithm that learns to solve TE problems efficiently in large-scale network scenarios, while achieving superior generalizability across diverse network conditions. TELGEN is based on the novel idea of transforming the problem of "predicting the optimal TE solution" into "predicting the optimal TE algorithm", which enables TELGEN to learn and efficiently approximate the end-to-end solving process of classical optimal TE algorithms. The learned algorithm is agnostic to the exact underlying network topology or traffic patterns, and is able to very efficiently solve TE problems given arbitrary inputs and generalize well to unseen topologies and demands. We train and evaluate TELGEN with random and real-world topologies, with networks of up to 5000 nodes and 3.6x10^6 links in testing. TELGEN shows less than 3% optimality gap while ensuring feasibility in all testing scenarios, even when the test network has 2-20x more nodes than the largest training network. It also saves up to 84% TE solving time than traditional interior-point method, and reduces up to 79.6% training time per epoch than the state-of-the-art learning-based algorithm.

翻译：在云广域网（WAN）和低地球轨道（LEO）卫星星座等超大规模网络中，流量工程（TE）是一项关键挑战。尽管已有基于学习的方法被提出以解决传统TE算法的可扩展性问题，但其实际应用常受限于泛化能力不足、训练开销过高以及未能严格遵守链路容量约束。本文提出TELGEN，一种新颖的TE算法，它能够学习在超大规模网络场景中高效求解TE问题，并在多样化的网络条件下实现卓越的泛化性能。TELGEN基于一种创新思路：将“预测最优TE解”的问题转化为“预测最优TE算法”，这使得TELGEN能够学习并高效逼近经典最优TE算法的端到端求解过程。所学算法不依赖于具体的底层网络拓扑或流量模式，能够在给定任意输入时高效求解TE问题，并对未见过的拓扑和流量需求展现出良好的泛化能力。我们使用随机及真实世界拓扑对TELGEN进行训练和评估，测试中网络规模高达5000个节点和3.6×10^6条链路。即使在测试网络的节点数比最大训练网络多2至20倍的情况下，TELGEN在所有测试场景中均保证解的有效性，且最优性差距小于3%。与传统内点法相比，其节省了高达84%的TE求解时间；与最先进的基于学习算法相比，每轮训练时间减少了高达79.6%。