A phase expansion for non-Markovian availability models with time-based aperiodic rejuvenation and checkpointing
Junjun Zheng, Hiroyuki Okamura, Tadashi Dohi · Communication in Statistics- Theory and Methods · 2020
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.