2022/10/26 by Luca Beurer-Kellner, Beurer-Kellner, Luca, Martin Vechev +5 · 1 citation
Computer Science · Materials Science · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning in Materials Science #Networking and Internet Architecture (cs.NI) #Software Engineering Research #Software System Performance and Reliability
paper · pdf · doi:10.48550/arxiv.2211.01980
openalex publication_date 2022/10/26 · openalex created_date 2022/11/09 · openalex updated_date 2026/07/28
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.