A phase expansion for non-Markovian availability models with time-based aperiodic rejuvenation and checkpointing
A phase expansion for non-Markovian availability models with time-based aperiodic rejuvenation and checkpointing
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DOI：
10.1080/03610926.2019.1708400
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发表时间：
2020-01
期刊：
影响因子：
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通讯作者：
Junjun Zheng;H. Okamura;T. Dohi
中科院分区：
文献类型：
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作者：
Junjun Zheng;H. Okamura;T. Dohi
摘要本文提出了一个随机框架，该框架由随机奖励网（SRN）组成，用于捕获系统的瞬态行为及其相关的非马克维亚国家过渡图，以建模一种操作软件系统，该系统经过基于时间基于时间的回复和检查方案的操作软件系统，进一步研究是否存在最佳的最佳恢复计划，以最大化系统的稳态可用性。采用了一种相扩展方法来解决非马克维亚的可用性模型，实际上，该模型既不是半马尔可夫过程，也不是马尔可夫再生过程。我们的数值结果表明，适当的复兴触发时间范围，从而对数据库系统的系统可用性产生了积极的改进效果，并且存在最佳的恢复触发触发时机，从而最大程度地提高了系统可用性。
Abstract This paper presents a stochastic framework, consisting of stochastic reward net (SRN) for capturing the transient behaviors of the system and its related non-Markovian state transition diagram, to model an operational software system that undergoes aperiodic time-based rejuvenation and checkpointing schemes, and further to investigate whether there exists the optimal rejuvenation schedule that maximizes the system steady-state availability. A phase expansion approach is adopted to solve the non-Markovian availability models, which are actually neither the semi-Markov processes nor the Markov regenerative processes. Our numerical results show an appropriate rejuvenation trigger timing range, resulting in the positive improvement effect on the system availability of a database system, and that there exists the optimal rejuvenation trigger timing maximizing the system availability.