Optimal Periodic Software Rejuvenation Policies in Discrete Time—Survey and Applications

2018 ◽  
pp. 81-108
Author(s):  
Tadashi Dohi ◽  
Junjun Zheng ◽  
Hiroyuki Okamura
Keyword(s):  
Discrete Time ◽  
Software Rejuvenation

ESTIMATING DISCRETE-TIME PERIODIC SOFTWARE REJUVENATION SCHEDULES UNDER COST EFFECTIVENESS CRITERION

2006 ◽  
Vol 13 (06) ◽  
pp. 565-579
Author(s):  
KAZUKI IWAMOTO ◽  
TADASHI DOHI ◽  
NAOTO KAIO
Keyword(s):  
Asymptotic Behavior ◽  
Cost Effectiveness ◽  
Discrete Time ◽  
Parametric Method ◽  
Regenerative Process ◽  
Numerical Examples ◽  
Software Aging ◽  
Software Rejuvenation ◽  
Markov Regenerative Process ◽  
Time Periodic

Software rejuvenation is a preventive and proactive solution that is particularly useful for counteracting the phenomenon of software aging. In this article, we consider the similar periodic software rejuvenation model to Garg et al.13 under the different operation circumstance. That is, we model the stochastic behavior of telecommunication billing applications by using a discrete-time Markov regenerative process, and determine the optimal periodic software rejuvenation schedule maximizing the so-called cost effectiveness, in discrete-time setting. Also, we provide a statistically non-parametric method to estimate the optimal software rejuvenation schedule, based on the discrete total time on test concept. Numerical examples are devoted to illustrate the determination/estimation of the optimal software rejuvenation schedule and to examine the asymptotic behavior of the estimator developed here.

The Effect of Partly Missing Covariates on Statistical Power in Randomized Controlled Trials With Discrete-Time Survival Endpoints

Methodology ◽  
2017 ◽  
Vol 13 (2) ◽  
pp. 41-60
Author(s):  
Shahab Jolani ◽  
Maryam Safarkhani
Keyword(s):  
Randomized Controlled Trials ◽  
Discrete Time ◽  
Treatment Effect ◽  
Survival Data ◽  
Controlled Trials ◽  
Missing Covariates ◽  
Indicator Method ◽  
Imputation Methods ◽  
Randomized Controlled ◽  
Baseline Covariates

Abstract. In randomized controlled trials (RCTs), a common strategy to increase power to detect a treatment effect is adjustment for baseline covariates. However, adjustment with partly missing covariates, where complete cases are only used, is inefficient. We consider different alternatives in trials with discrete-time survival data, where subjects are measured in discrete-time intervals while they may experience an event at any point in time. The results of a Monte Carlo simulation study, as well as a case study of randomized trials in smokers with attention deficit hyperactivity disorder (ADHD), indicated that single and multiple imputation methods outperform the other methods and increase precision in estimating the treatment effect. Missing indicator method, which uses a dummy variable in the statistical model to indicate whether the value for that variable is missing and sets the same value to all missing values, is comparable to imputation methods. Nevertheless, the power level to detect the treatment effect based on missing indicator method is marginally lower than the imputation methods, particularly when the missingness depends on the outcome. In conclusion, it appears that imputation of partly missing (baseline) covariates should be preferred in the analysis of discrete-time survival data.

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