不确定性随机系统分析中的构造均匀性

H. Hermanns, S. Johr
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引用次数: 28

摘要

连续时间马尔可夫决策过程(ctmdp)是具有连续时间、不确定性和无记忆随机性的行为模型。最近,提出了一种有效的定时可达算法,允许人们量化,例如,在安全关键任务时间内达到不安全系统状态的最坏概率。该算法仅适用于均匀CTMDPs,即逗留时间分布在所有状态中是唯一的CTMDPs。本文提出了一种构造均匀的CTMDPs合成理论。为了分析该方法的可扩展性,将该理论应用于一个容错工作站集群实例的构建,并使用一种创新的定时可达算法进行实验评估。由于缺乏对建模和分析的支持,之前所有对这个看似研究得很好的例子进行模型检验的尝试都需要忽略不确定性的存在。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Uniformity by Construction in the Analysis of Nondeterministic Stochastic Systems
Continuous-time Markov decision processes (CTMDPs) are behavioral models with continuous-time, nondeterminism and memoryless stochastics. Recently, an efficient timed reachability algorithm for CTMDPs has been presented, allowing one to quantify, e. g., the worst-case probability to hit an unsafe system state within a safety critical mission time. This algorithm works only for uniform CTMDPs -- CTMDPs in which the sojourn time distribution is unique across all states. In this paper we develop a compositional theory for generating CTMDPs which are uniform by construction. To analyze the scalability of the method, this theory is applied to the construction of a fault-tolerant workstation cluster example, and experimentally evaluated using an innovative implementation of the timed reachability algorithm. All previous attempts to model-check this seemingly well-studied example needed to ignore the presence of nondeterminism, because of lacking support for modelling and analysis.
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