Reliability of systems with Markov transfer of control, II

Siegrist, Kyle

doi:10.1109/32.6192

Cited by 20 publications

(7 citation statements)

References 4 publications

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“…The state space models used to represent application architecture include a discrete-time Markov chain (DTMC) [8], [82], [83], [31], a continuous time Markov chain (CTMC) [53], or a semi-Markov process [58]. Although the pathbased approaches represent the failure behavior of the components using the probability of failure or reliability, the state-based approaches allow component failure behavior to be represented using three types of failure models, namely, probability of failure or reliability [8], [31], [82], [83], constant failure rate [53], and time-dependent failure intensity [32]. These failure models can be viewed to form a hierarchy, as far as the level of detail that can be incorporated and the accuracy of the reliability estimate are produced.…”

Section: State-of-the-art: Overviewmentioning

confidence: 99%

Architecture-Based Software Reliability Analysis: Overview and Limitations

Gokhale

2007

IEEE Trans. Dependable and Secure Comput.

171

121

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Section: State-of-the-art: Overviewmentioning

confidence: 99%

Architecture-Based Software Reliability Analysis: Overview and Limitations

Gokhale

2007

IEEE Trans. Dependable and Secure Comput.

171

121

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“…Cheung [2] requires the probabilities of transitions between internal component states, while system-level approaches need the probabilities of transfer of control between components and services [3,12,19,35,36,20,30], or the probabilities of execution of particular execution paths [5,17,31,34,43]. Rodrigues [31] also requires the probabilities of transfer of control between execution paths.…”

Section: Reliability Parametersmentioning

confidence: 98%

“…A number of approaches estimate system reliability as a function of the reliabilities of individual components, without going into sufficient detail regarding the component interactions (e.g., [3,30,35,36,42,43]). Although such techniques can be scalable, they are not entirely satisfactory because they assume that reliabilities of individual components are known.…”

Section: Complexity In Architecture-based Reliability Estimationmentioning

confidence: 99%

A Comprehensive Exploration of Challenges in Architecture-Based Reliability Estimation

Krka

Edwards

Cheung

et al. 2009

Architecting Dependable Systems VI

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Abstract. Architecture-based reliability estimation is challenging: modern software is complex with numerous factors affecting a system's reliability. In this article, we address three core challenges for architecture-based estimation of a system's reliability: (1) defining an appropriate failure model based on characteristics of the system being analyzed, (2) dealing with uncertainties of the reliability-related parameters, due to the lack of system implementation, and (3) overcoming the barriers of complexity and scale inherent in modern software. For each challenge, we identify the essential elements of the problem space, outline promising solutions, and illustrate the problems and solutions using a robotics case study. First, we show how a failure model can be derived from the system requirements and architecture. Second, we suggest how information sources available during architectural design can be combined to mitigate model parameter uncertainties. Third, we foresee hierarchical techniques as a promising way of improving the computational tractability of reliability models.

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“…These systems can be modelled by a similar Markov chain, but with only one terminal state f [11]. Here the mean time to failure, expressed as a number of transitions before termination in the terminal state f , given that the system is presently in state i, can be calculated as…”

Section: Alternative System Modelmentioning

confidence: 99%

Sensitivity of software system reliability to usage profile changes

Weyns

Runeson

2007

Proceedings of the 2007 ACM Symposium on Applied Computing

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Usage profiles are an important factor in software system reliability estimation. To assess the sensitivity of a system's reliability to changes in the usage profile, a Markov based system model is used. With the help of this model, the statistical sensitivity to many independent changes can be estimated. The theory supports both absolute and relative changes and can be used for systems with or without a terminal state. With this approach it is possible to very quickly estimate the uncertainty on the predicted reliability calculated from a Markov model based upon the uncertainty on the usage profile. Finally the theory is applied to an example to illustrate its use and to show its validity.

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Reliability of systems with Markov transfer of control, II

Cited by 20 publications

References 4 publications

Architecture-Based Software Reliability Analysis: Overview and Limitations

Architecture-Based Software Reliability Analysis: Overview and Limitations

A Comprehensive Exploration of Challenges in Architecture-Based Reliability Estimation

Sensitivity of software system reliability to usage profile changes

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