网络系统的不变量发现
Invariant Discovery for Networked Systems
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中文总结 AI 辅助
研究网络系统不变量发现问题，核心方法是将其分为人工智能驱动的语法“发现”与统计驱动的“搜索”问题，设计实现Autogram系统，贡献是能在多种数据上高覆盖率、低误报地恢复专家不变量并讨论开放问题。
中文摘要 AI 辅助
不变量是网络测量信号之间预期成立的关系，支撑着从验证到流量生成、遥测插补和输入验证等应用，但手动编写需要形式逻辑和网络方面的专业知识。自动挖掘器有局限，大语言模型（LLMs）虽能提供语义推理但具有不确定性和不透明性。本文将不变量搜索问题分为人工智能驱动的语法“发现”问题和统计驱动的在所学语法内的“搜索”问题，设计并实现了Autogram系统，在公共和生产遥测数据上评估，能高覆盖率、低误报地恢复专家得出的不变量，最后讨论了完全开放式发现道路上的开放问题。
英文摘要
Invariants, the relations expected to hold among measured signals of a network, underpin applications from verification to traffic generation, telemetry imputation, and input validation, yet writing them by hand demands rare expertise in both formal logic and networking. Automatic miners can help but fall short on two fronts: they still require the hardest input (the grammar of admissible invariants) and they learn only exact, ``hard'' rules, struggling with real-world approximation caused by inherent noise in data. LLMs are tools that can provide semantic reasoning over data, but are non-deterministic and opaque in their learning. Our key idea is to partition the invariant search problem into an AI-driven grammar ``discovery'' problem, followed by a statistics-driven ``search'' problem within the learned grammar. Taken together, this allows non-deterministic, hallucination-prone AI to help produce auditable invariants with formal guarantees. We design and implement such a system, Autogram, and evaluate it on both public and production telemetry data, recovering expert-derived invariants with high coverage and low false positives. We close with discussion on open problems on the path toward fully open-ended discovery.
发表机构
- Princeton University（普林斯顿大学）
- UC Berkeley（加州大学伯克利分校）
- Google（谷歌公司）
机构由 AI 辅助整理，请以论文原文为准。
