Near-optimal Online Traffic Engineering

2026-05-15

AI Summary

Traditional wide-area network traffic engineering struggles to adapt to dynamic changes due to its centralized, periodic optimization mechanism, often resulting in minute-scale delays and suboptimal solutions. This work proposes OnlineTE, a distributed solver grounded in optimization decomposition theory that integrates centralized coordination with edge-driven execution. By enabling switches to immediately trigger re-optimization upon detecting link or traffic changes, OnlineTE achieves near-optimal path scheduling with second-scale responsiveness. The approach embeds multi-commodity load balancing (MLU) and max-flow problem formulations into a distributed algorithm, demonstrating in simulations with 750 nodes a tenfold performance improvement over the state-of-the-art while maintaining computational overhead well below the capacity of modern programmable switches.

Abstract

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.

Problem

• Traffic Engineering
• WAN
• Online Optimization
• Scalability
• Reactivity

Innovation

• Online Traffic Engineering
• Optimization Decomposition
• Distributed TE Solver
• Edge-based TE
• Near-optimal WAN Routing