AN ADAPTIVE COST-BASED SOFTWARE REJUVENATION SCHEME WITH NONPARAMETRIC PREDICTIVE INFERENCE APPROACH

Abstract

This paper proposes an approach to estimate an optimal software rejuvenation schedule minimizing an expected total software cost per unit time. Based on a non-parametric predictive inference (NPI) approach, we derive the upper and lower bounds of the predictive expected software cost via the predictive survival function from system failure time data, and characterize an adaptive cost-based software rejuvenation policy, from the system failure time data with a right-censored observation. In simulation experiments, it is shown that our NPI-based approach is quite useful to predict the optimal software rejuvenation time.