Analysis of software cost models with rejuvenation

Dohi, Tadashi; Goševa-Popstojanova, Katerina; Trivedi, Kishor S.

doi:10.1109/hase.2000.895436

Cited by 55 publications

(25 citation statements)

References 14 publications

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“…At any instant t, the conditional transition probability matrix V i,j (t) defined by Z(t) can be expressed as follows [8], [10]:…”

Section: Z(t) Is Called An Mrgp If It Identifies An Emrs (X S) With mentioning

confidence: 99%

“…The analytical model-based approach uses probability models to estimate the software aging process and determine the optimal rejuvenation scheduling based on the estimated aging state [1], [10]- [12]. On the other hand, measurement-based approaches collect information directly from the system and estimates using statistical or machine learning methods the real state of the system to trigger the rejuvenation process accordingly [13]- [23].…”

Section: Introductionmentioning

confidence: 99%

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Optimization of Two-Granularity Software Rejuvenation Policy Based on the Markov Regenerative Process

et al. 2016

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. Abstract-Software rejuvenation is a proactive software control technique that is used to improve a computing system performance when it suffers from software aging. In this paper, a two-granularity inspection-based software rejuvenation policy, which works as a closed-loop control technique, is proposed. This policy mitigates the negative impact of two-level software aging. The two levels considered are the user-level applications and the operating system. A Markov regenerative process model is constructed based on the system condition. We obtain the degradation rate of the application software and operating system from fault injection experiments. The diagnostic accuracy of the adopted monitor and analysis system, which is applied to inspect the application software and operating system, is considered as we provide the optimal rejuvenation strategies. Finally, the availability and the overall loss probability with their corresponding optimal inspection time intervals are obtained numerically based on the parameter values estimated from the experiments. Experimental results show that two-granularity software rejuvenation is much more effective than traditional single-level software rejuvenation. In our experimental study, when two-granularity software rejuvenation is used, the unavailability and the overall loss probability of the system were reduced by 17.9% and 2.65%, respectively, in comparison with the single-level rejuvenation.Index Terms-Diagnostic accuracy, Markov regenerative process (MRGP), multigranularity software aging, overall loss probability, software rejuvenation.

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“…At any instant t, the conditional transition probability matrix V i,j (t) defined by Z(t) can be expressed as follows [8], [10]:…”

Section: Z(t) Is Called An Mrgp If It Identifies An Emrs (X S) With mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization of Two-Granularity Software Rejuvenation Policy Based on the Markov Regenerative Process

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Furthermore, it is now well known that currently, computer system outages are more often due to software faults, and not hardware [1], [2]. Several studies [3], [4], [5] have showed that software aging phenomena is one of the sources of the unavailability. Software aging phenomena refers to the accumulation of errors, usually provoking resource contention, during long running application executions, like web applications, which normally cause applications/systems hang or crash [6].…”

Section: Introductionmentioning

confidence: 99%

J2EE instrumentation for software aging root cause application component determination with AspectJ

Alonso

Torres

Berral

et al. 2010

2010 IEEE International Symposium on Parallel &Amp; Distributed Processing, Workshops and PHD Forum (IPDPSW)

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Abstract-Unplanned system outages have a negative impact on company revenues and image. While the last decades have seen a lot of efforts from industry and academia to avoid them, they still happen and their impact is increasing. According to many studies, one of the most important causes of these outages is software aging. Software aging phenomena refers to the accumulation of errors, usually provoking resource contention, during long running application executions, like web applications, which normally cause applications/systems hang or crash. Determining the software aging root cause failure, not the resource or resources involved in, is a huge task due to the growing day by day complexity of the systems. In this paper we present a monitoring framework based on Aspect Programming to monitor the resources used by every application component in runtime. Knowing the resources used by every component of the application we can determine which component or components are related to the software aging. Furthermore, we present a case study where we evaluate our approach to determine in a web application scenario which component/s are involved in the software aging with promising results.

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“…Several studies [5,6] have reported that one of the causes of the unplanned software outages is the software aging phenomena. This term refers to the accumulation of errors, usually provoking resource contention, during long running application executions, like web applications, which nor mally cause applications/systems hang or crash [7].…”

Section: Introductionmentioning

confidence: 99%

Adaptive on-line software aging prediction based on machine learning

Alonso

Torres

Berral

et al. 2010

2010 IEEE/IFIP International Conference on Dependable Systems &Amp; Networks (DSN)

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The growing complexity of software systems is resulting in an increasing number of software faults. According to the literature, software faults are becoming one of the main sources of unplanned system outages, and have an impor tant impact on company benefits and image. For this rea son, a lot of techniques (such as clustering, fail-over tech niques, or server redundancy) have been proposed to avoid software failures, and yet they still happen. Many software failures are those due to the software aging phenomena. In this work, we present a detailed evaluation of our chosen machine learning prediction algorithm (M5P) in front of dynamic and non-deterministic software aging. We have tested our prediction model on a three-tier web 12EE ap plication achieving acceptable prediction accuracy against complex scenarios with small training data sets. Further more, we have found an interesting approach to help to de termine the root cause failure: The model generated by ma chine learning algorithms.

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Analysis of software cost models with rejuvenation

Cited by 55 publications

References 14 publications

Optimization of Two-Granularity Software Rejuvenation Policy Based on the Markov Regenerative Process

Optimization of Two-Granularity Software Rejuvenation Policy Based on the Markov Regenerative Process

J2EE instrumentation for software aging root cause application component determination with AspectJ

Adaptive on-line software aging prediction based on machine learning

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