论文标题
部分可观测时空混沌系统的无模型预测
Learning to Configure Computer Networks with Neural Algorithmic Reasoning
论文作者
论文摘要
我们提出了一种用于缩放计算机网络自动配置的新方法。关键的想法是放松在计算上的硬搜索问题，即找到满足给定规范的配置，以适合基于学习的技术的近似目标。基于这个想法，我们训练一个神经算法模型，该模型学会生成可能（完全或部分）满足现有路由协议下给定规范的配置。通过放松刚性满意度的保证，我们的方法（i）可以提高灵活性：它是协议 - 不合时宜的，可以启用交叉协议推理，并且不依赖于硬编码的规则； （ii）找到比以前可能更大的计算机网络的配置。我们学到的合成器比基于SMT的最先进的方法快490倍，同时产生的配置平均满足了提供的要求的93％以上。
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.
