Statistical non-parametric algorithms to estimate the optimal software rejuvenation schedule

In this paper, we extend the classical result by Huang, Kintala, Kolettis and Fulton (1995), and in addition propose a modified stochastic model to determine the software rejuvenation schedule. More precisely, the software rejuvenation models are formulated via the semi-Markov processes, and the optimal software rejuvenation schedules which maximize the system availabilities are derived analytically for respective cases. Further, we develop nonparametric statistical algorithms to estimate the optimal software rejuvenation schedules, provided that the statistical complete (unsensored) sample data of failure times is given. In numerical examples, we examine asymptotic properties for the statistical estimation algorithms.

• Type: Conference
• Year: 2000
• Where: PRDC
• Authors: Tadashi Dohi, Katerina Goseva-Popstojanova, Kishor S. Trivedi
• Keywords: Applied Computing, Optimal Software Rejuvenation, PRDC 2000, Software Rejuvenation Models, Software Rejuvenation Schedules