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.

翻译：我们为计算机网络的自动配置提出了一个新的缩放方法。 关键的想法是, 将找到符合特定规格的配置的计算硬搜索问题放松到适合学习技术的近似目标。 基于这个想法, 我们训练一个神经算法模型, 学习生成可能( 全部或部分) 在现有路线协议下满足特定规格的配置。 通过放松严格的满意度保证, 我们的方法 (一) 使得更大的灵活性: 它属于协议性, 能够跨程序推理, 并且不依赖于硬编码规则; 以及 (二) 找到比以前可能大得多的计算机网络的配置。 我们所学的合成器比最新的SMT方法快到490x, 同时产生平均满足93%以上要求的配置。