This paper presents the opportunity-based software rejuvenation policy and the optimization problem of software rejuvenation trigger time maximizing the system performance index. Our model is based on a basic semi-Markov software rejuvenation model by Dohi et al. 2000 under the environment where possible time, called opportunity, to execute software rejuvenation is limited. In the paper, we consider two stochastic point processes; renewal process and Markovian arrival process to represent the opportunity process. In particular, we derive the existence condition of the optimal trigger time under the two point processes analytically. In numerical examples, we illustrate the optimal design of the rejuvenation trigger schedule based on empirical data.

Software rejuvenation is a preventive and proactive solution that is particularly useful for counteracting the phenomenon of software aging. In this article, we consider periodic software rejuvenation models based on the expected cost per unit time in the steady state under discrete-time operation circumstance. By applying the discrete renewal reward processes, we describe the stochastic behavior of a telecommunication billing application with a degradation mode, and determine the optimal periodic software rejuvenation schedule minimizing the expected cost. Similar to the earlier work by the same authors, we develop a statistically non-parametric algorithm to estimate the optimal software rejuvenation schedule, by applying the discrete total time on test concept. Numerical examples are presented to estimate the optimal software rejuvenation schedules from the simulation data. We discuss the asymptotic behavior of estimators developed in this paper.

In recent years, considerable attention has been devoted to continuously running software systems whose performance characteristics are smoothly degrading in time. Software aging often affects the performance of a software system and eventually causes it to fail. A novel approach to handle transient software failures due to software aging is called software rejuvenation, which can be regarded as a preventive and proactive solution that is particularly useful for counteracting the aging phenomenon. In this paper, we focus on a high assurance software system with fault-tolerance and preventive rejuvenation, and analyze the stochastic behavior of such a highly critical software system. More precisely, we consider a fault-tolerant software system with two-version redundant structure and random rejuvenation schedule, and evaluate quantitatively some dependability measures like the steady-state system availability and MTTF based on the familiar Markovian analysis. In numerical examples, we examine the dependence of two fault tolerant techniques; design and environment diversity techniques, on the system dependability measures.

Software rejuvenation is a preventive and proactive solution that is particularly useful for counteracting the phenomenon of software aging. In this paper, we consider both the periodic and non-periodic software rejuvenation policies under different dependability measures. As is well known, the steady-state system availability is the probability that the software system is operating in the steady state and, at the same time, is often regarded as the mean up rate in the system operation period. We show that the mean up rate should be defined as the mean value of up rate, but not as the mean up time per mean operation time. We derive numerically the optimal software rejuvenation policies which maximize the steady-state system availability and the mean up rate, respectively, for each periodic or non-periodic model. Numerical examples show that the real mean up rate is always smaller than the system availability in the steady state and that the availability overestimates the ratio of operative time of the software system.

The software rejuvenation is a proactive fault management technique for operational software systems which age due to the error conditions that accrue with time and/or load, and is important for high assurance systems design. In this paper, fine-grained shock models are developed to determine the optimal rejuvenation policies which maximize the system availability. We introduce three kinds of rejuvenation schemes and calculate the optimal software rejuvenation schedules maximizing the system availability for respective schemes. The stochastic models with three rejuvenation policies are extentions of Bobbio et al. (1998, 2001) and represent the failure phenomenon due to the exhaustion of the software resources caused by the memory leak, the fragmentation, etc. Numerical examples are devoted to compare three control schemes quantitatively.

The software rejuvenation is one of the most effective preventive maintenance technique for operational software systems with high assurance requirement. In this paper, we propose the workload-based software rejuvenation scheme for a server type of software system, and develop stochastic models to determine the optimal software rejuvenation schedules for some dependability measures. In numerical examples, we evaluate quantitatively the performance of workload-based software rejuvenation scheme and compare it with the time-based rejuvenation scheme.