Analysis of software rejuvenation using Markov Regenerative Stochastic Petri Net

Garg, Sachin; Puliafito, Antonio; Telek, Miklós; Trivedi, Kishor S.

doi:10.1109/issre.1995.497656

Cited by 176 publications

(87 citation statements)

References 12 publications

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“…Dohi et al [13] models optimal rejuvenation schedule using semi-Markov processes to maximize availability and minimize cost. The focus here is aging caused due to processing attributes; however, unlike this work we focus on the functionality attributes of a system Garg et al [14]. Adopts the periodic rejuvenation technique proposed by Huang et al [6] and uses stochastic petri net to model stochastic behavior of software aging.…”

Section: Related Workmentioning

confidence: 99%

A Simple Software Rejuvenation Framework Based on Model Driven Development

Mahmud

2017

UHDJST

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A B S T R A C TIn the current dynamic-natured business environment, it is inevitable that today's software systems may not be suitable for tomorrow's business challenges which indicate that the software in use has aged. Although we cannot prevent software aging, we can try to prolong the aging process of software so that it can be used for longer. In this paper, we outline a conceptual software rejuvenation framework based on model driven development approach. The framework is simple but effective and can be implemented in a recursive five step process. We have illustrated the applicability of the framework using a simple business case study which highlights the effectiveness of the framework. This work adds to the existing literature on software aging and its preventative measures. It also fills in the research gap which exists about software aging caused by changing requirements.

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Section: Related Workmentioning

confidence: 99%

A Simple Software Rejuvenation Framework Based on Model Driven Development

Mahmud

2017

UHDJST

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“…Some of the techniques described above can either be scheduled periodically or system state dependent. For periodic triggering several approaches exist to determine the optimal cycle duration: For example, Dohi et al [4] use semi-Markov models, Pfening et al [18] use a Markov decision process and Garg et al [19] employ a Markov Regenerative Stochastic Petri Net (MRSPN). Trivedi et al [20] show that state-dependent application of proactive recovery mechanisms has the potential to be more appropriate and hence more effective than the periodic alternative.…”

Section: Preventive Maintenancementioning

confidence: 99%

Prediction-Based Software Availability Enhancement

Salfner

Hoffmann

Malek

2005

Self-Star Properties in Complex Information Systems

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Abstract. We propose a new paradigm for software availability enhancement. We offer a two-step strategy: Failure prediction followed by maintenance actions with the objective of avoiding impending failures or minimizing the effort of their repair. For the first step we present two failure prediction methods: universal basis functions (UBF) and similar events prediction (SEP), which are based on probabilistic analysis. The potential of the presented methods is evaluated by a case-study where failures of a commercial telecommunication platform have been predicted. The second step includes existing maintenance methods fitting the proposed approach and a new recovery strategy called "adaptive recovery blocks". Since system availability enhancement is the overall goal, equations to calculate availability of such a system are given as well.

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“…Rinsaka and Dohi [33,35] use a non-parametric predictive inference (NPI) approach by Coolen and Yan [10] and Coolen-Schrijner and Coolen [11]. They focus on a periodic software rejuvenation model Garg et al [20] and Suzuki et al [37] and derive the optimal NPI-based software rejuvenation time maximizing the steady-state availability criterion. In fact, the NPI approaches have been applied to a number of reliability and maintenance problems (Coolen-Schrijner et al [12], Coolen-Maturi and Coolen [13], Aboalkhai et al [1,2], Venkat et al [41]).…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Cost-Based Software Rejuvenation Scheme With Nonparametric Predictive Inference Approach

Rinsaka

2017

JORSJ

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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.

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Analysis of software rejuvenation using Markov Regenerative Stochastic Petri Net

Cited by 176 publications

References 12 publications

A Simple Software Rejuvenation Framework Based on Model Driven Development

A Simple Software Rejuvenation Framework Based on Model Driven Development

Prediction-Based Software Availability Enhancement

An Adaptive Cost-Based Software Rejuvenation Scheme With Nonparametric Predictive Inference Approach

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