Near-optimal Online Traffic Engineering

TL;DR

OnlineTE is a distributed system for WAN traffic engineering that reacts instantly to demand changes, providing near-optimal solutions within seconds and outperforming existing methods significantly.

Contribution

The paper introduces OnlineTE, a scalable, distributed traffic engineering system that enables immediate re-optimization at switches, improving responsiveness and efficiency.

Findings

OnlineTE reacts within seconds to demand changes and failures.

On a 750-node WAN testbed, OnlineTE outperforms state-of-the-art methods by up to ten times.

OnlineTE scales efficiently to large WANs with low computational requirements.

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.