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Rare Event Simulation for Highly Dependable Systems with Fast Repairs

机译:具有快速修复功能的高度可靠系统的稀有事件模拟

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Stochastic model checking has been used recently to assess, among others, dependability measures for a variety of systems. However, the employed numerical methods, as, e.g., supported by model checking tools such as PRISM and MRMC, suffer from the state-space explosion problem. The main alternative is statistical model checking, which uses standard simulation, but this performs poorly when small probabilities need to be estimated. Therefore, we propose a method based on importance sampling to speed up the simulation process in cases where the failure probabilities are small due to the high speed of the systemȁ9;s repair units. This setting arises naturally in Markovian models of highly dependable systems. We show that our method compares favourably to standard simulation, to existing importance sampling techniques and to the numerical techniques of PRISM.
机译:最近,随机模型检查已用于评估各种系统的可靠性度量。但是,例如由诸如PRISM和MRMC之类的模型检验工具支持的所采用的数值方法遭受状态空间爆炸问题的困扰。主要替代方法是使用标准模拟的统计模型检查,但是在需要估计小概率时,这种方法的性能较差。因此,我们提出了一种基于重要性抽样的方法,以在由于系统维修速度较高而导致故障概率较小的情况下加快仿真过程。这种设置在高度可靠的系统的马尔可夫模型中自然而然地出现。我们表明,我们的方法与标准仿真,现有的重要抽样技术和PRISM的数值技术相比具有优势。

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