A phase expansion for non-Markovian availability models with time-based aperiodic rejuvenation and checkpointing

Junjun Zheng, Hiroyuki Okamura and Tadashi Dohi

Communications in Statistics - Theory and Methods, 2020, vol. 49, issue 15, 3712-3729

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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:49:y:2020:i:15:p:3712-3729

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DOI: 10.1080/03610926.2019.1708400

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