Most deployed WAN Traffic Engineering (TE) systems use a logically centralized controller that periodically gathers traffic demands, runs a TE optimization or heuristic, and then programs the network. At scale, these solutions can be sub-optimal, and can take minutes to react to demand changes or failures. In this paper, we introduce OnlineTE, a system that reacts immediately to demand changes and failures, and delivers near-optimal solutions within seconds of a change. OnlineTE builds on the theory of optimization decomposition to devise scalable, near-optimal, distributed TE solvers for path-based MLU and Max-flow problems. In OnlineTE, each switch solves part of the optimization, and a central coordinator orchestrates the progress of the switches. As such, a switch can trigger a re-optimization as soon as it notices a demand change or failure, enabling high reactivity. OnlineTE scales to large WANs, and its compute requirements are well below the capabilities of modern WAN switches. It also enables a new opportunity, edge-based TE, which can utilize resources more efficiently than today's path-based approaches. On a testbed emulation of a 750-node WAN topology, OnlineTE can outperform the state-of-the-art by up to an order of magnitude.
@article{arxiv.2605.16187,
title = {Near-optimal Online Traffic Engineering},
author = {Arvin Ghavidel and Pooria Namyar and Nikolai Matni and Walter Willinger and Ramesh Govindan},
journal= {arXiv preprint arXiv:2605.16187},
year = {2026}
}