Distributed programs are often designed with implicit assumptions about the underlying system. We focus on assumptions related to clock synchronization. When a program written with clock synchronization assumptions is monitored to determine if it satisfies its requirements, the monitor should also account for these assumptions precisely. Otherwise, the monitor will either miss potential bugs (false negatives) or find bugs that are inconsistent with these assumptions (false positives). However, if assumptions made by the program are implicit or change over time and are not immediately available to the monitor, such false positives and/or negatives are unavoidable. This paper characterizes precision (probability that the violation identified by the monitor is valid) and recall (probability that the monitor identifies an actual violation) of the monitor based on the gap between clock synchronization assumptions made by the program/application and the clock synchronization assumptions made by the monitor. Our analysis is based on the development of an analytical model for precision, recall and sensitivity of monitors detecting conjunctive predicates. We validate the model via simulations and experiments on the Amazon Web Services platform.

分布式程序的设计通常带有对底层系统的隐含假设。我们专注于与时钟同步相关的假设。当使用时钟同步假设编写的程序被监控以确定它是否满足其要求时，监控器还应该准确地考虑这些假设。否则，监视器要么错过潜在的错误（误报），要么发现与这些假设不一致的错误（误报）。但是，如果程序所做的假设是隐含的或随着时间的推移而改变，并且不能立即提供给监视器，则此类误报和/或否定是不可避免的。本文根据程序/应用程序做出的时钟同步假设与时钟同步假设之间的差距来表征监视器的精度（监视器识别出的违规有效的概率）和召回率（监视器识别出实际违规的概率）监视器制作的。我们的分析基于检测连接谓词的监视器的精确度、召回率和灵敏度分析模型的开发。我们通过 Amazon Web Services 平台上的模拟和实验来验证模型。检测连接谓词的监视器的召回和敏感性。我们通过 Amazon Web Services 平台上的模拟和实验来验证模型。检测连接谓词的监视器的召回和敏感性。我们通过 Amazon Web Services 平台上的模拟和实验来验证模型。