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Component Importance Measures for Real-Time Computing Systems in the Presence of Common-Cause Failures

机译:在存在常见故障的情况下实时计算系统的组件重要性措施

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Component importance analysis is to measure the effect on system reliability of component reliabilities, and it can be used to the design of system from the reliability point of view. In this paper, we consider the component importance analysis of real-time computing systems in the presence of common-cause failures (CCFs) (i.e., failure dependencies). Although the CCFs are known as a risk factor of degradation of system reliability, it is difficult to evaluate the component importance measures in the presence of CCFs analytically. This paper introduces a continuous-time Markov chain (CTMC) model for real-time computing system, and applies the CTMC-based component-wise sensitivity analysis which can evaluate the component importance measures without any structure function of system. Also, in numerical experiments, we evaluate the effect of CCFs by the comparison of system performance measures and component importance in the case of system with CCFs with those in the case that there is no CCF in the system.
机译:组件重要性分析是测量对元件可靠性的系统可靠性的影响,它可用于从可靠性视角设计系统。在本文中,我们考虑在存在公共故障(CCF)(即,失败依赖性)中的实时计算系统的组件重要性分析。虽然CCF被称为系统可靠性降低的风险因素,但难以分析CCFS在CCF存在下进行评估。本文介绍了一种用于实时计算系统的连续时间马尔可夫链(CTMC)模型,并应用基于CTMC的组件 - 明智的灵敏度分析,可以评估组件重要性测量而不提供系统的任何结构功能。此外,在数值实验中,我们通过在系统中具有CCF的系统的情况下,评估CCFS对系统性能测量和组件重要性的影响,其中包括系统中没有CCF的CCF。

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