Learning to Configure Computer Networks with Neural Algorithmic Reasoning
Abstract
We present a new method for scaling automatic configuration of computer networks. The key idea is to relax the computationally hard search problem of finding a configuration that satisfies a given specification into an approximate objective amenable to learning-based techniques. Based on this idea, we train a neural algorithmic model which learns to generate configurations likely to (fully or partially) satisfy a given specification under existing routing protocols. By relaxing the rigid satisfaction guarantees, our approach (i) enables greater flexibility: it is protocol-agnostic, enables cross-protocol reasoning, and does not depend on hardcoded rules; and (ii) finds configurations for much larger computer networks than previously possible. Our learned synthesizer is up to 490x faster than state-of-the-art SMT-based methods, while producing configurations which on average satisfy more than 93% of the provided requirements.
Cite
@article{arxiv.2211.01980,
title = {Learning to Configure Computer Networks with Neural Algorithmic Reasoning},
author = {Luca Beurer-Kellner and Martin Vechev and Laurent Vanbever and Petar Veličković},
journal= {arXiv preprint arXiv:2211.01980},
year = {2022}
}