Learning to Configure Computer Networks with Neural Algorithmic Reasoning
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Author / Producer
Date
2022
Publication Type
Conference Paper
ETH Bibliography
yes
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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.
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Publication status
published
Book title
Advances in Neural Information Processing Systems 35
Journal / series
Volume
Pages / Article No.
730 - 742
Publisher
Curran
Event
36th Annual Conference on Neural Information Processing Systems (NeurIPS 2022)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
09477 - Vanbever, Laurent / Vanbever, Laurent
03948 - Vechev, Martin / Vechev, Martin
Notes
Poster presentation on December 1, 2022.
Funding
ETH-03 19-2 - Dependable and Data-Driven Intelligent Networks (ETHZ)