SURF A PROGRAM FOR DEPENDABILITY EVALUATlON OF COMPLEX FAULT-TOLERANT COMPUTING SYSTEMS

Costes, Alain; Doucet, J.; Landrault, C.; Laprie, Jean-Claude

doi:10.1109/ftcsh.1995.532629

Cited by 12 publications

(8 citation statements)

References 5 publications

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“…The purpose'of this correspondence is to call to attention recent results [4], [5] which simplify calculation of MTTF and higher moments of the time to system failure. The particular case Qf MTTF was also obtained by Laprie a,nd is reported in [2]; however, it is argued that the higher moments are of equal interest, e.g., in calculating MRL and its variance. With the reported result, it becomes quite feasible to run parametric studies on these other measures as well.…”

mentioning

confidence: 56%

“…Then it can be shown that (8) Manuscript The special case of this formula for MTTF (n = 1) was also derived by Laprie solving the forward Kolmogorov equations; it appears in [2].…”

Section: A Simplified Methodsmentioning

confidence: 95%

“…Similarly, the second moment is 2 Arnold appears to have made a computational error in calculating the MTTF-his numerator has the equivalent of I + h(2 + 3q). This does not, however, invalidate his results because he is mainly interested in the cases for which 6 << 1; under this condition, both expressions reduce to the same approximation.…”

Section: A Simplified Methodsmentioning

confidence: 99%

“…[101 J. Vuillemin, "A combinatorial limit to the computing power of VLSI [2]. Systenms both with and without repair may be modeled this way for calculation of reliability, mean time to failure (MTTF), mission time, mean time to repair, mean residual lifetime (MRL), and other important measures.…”

mentioning

confidence: 99%

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A Simplified Method to Calculate Failure Times in Fault-Tolerant Systems

Seth

Lipsky²

1983

IEEE Trans. Comput.

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mentioning

confidence: 56%

“…Then it can be shown that (8) Manuscript The special case of this formula for MTTF (n = 1) was also derived by Laprie solving the forward Kolmogorov equations; it appears in [2].…”

Section: A Simplified Methodsmentioning

confidence: 95%

Section: A Simplified Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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A Simplified Method to Calculate Failure Times in Fault-Tolerant Systems

Seth

Lipsky²

1983

IEEE Trans. Comput.

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“…Phase approximations have been discussed in the context of reliability by Singh et a1 [37], and Bobbio et a1 17,8,91. Phase approximations were used in the SURF package [14,181, although SURF was intended only for a restricted class of reliability models. Phase approximations were also used in the ESP package [16].…”

Section: Introductionmentioning

confidence: 99%

Selecting and implementing phase approximations for semi-Markov models

Malhotra¹,

Reibman²

1993

Communications in Statistics. Stochastic Models

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Markov chains are commonly used for reliability, availability, and performability modeling. A central assumption in Markov reliability analysis is that failure and repair-time distributions are exponential. Unfortunately, in many real-life applications, assuming exponential distributions can be a significant oversimplification. Semi-Markov chains provide a simple mathematical structure for including general distributions in the Markov model framework. Three methods for analyzing semi-Markov chains are numerical solution, discreteevent simulation, and phase approximation. In this paper, we discuss a complete approach to phase approximation, including choice of phase approximation class, numerical fitting of appropriate parameters, and implementation of the approximation approach in a modeling toolkit. We describe a new hybrid approach for parameter fitting that combines moment-matching with least-squares fitting.

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Dependability and performability analysis

Trivedi

Ciardo

Malhotra

et al.

Performance Evaluation of Computer and Communication Systems

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In this tutorial, we discuss several practical issues regarding specification and solution of dependability and performability models. We compare model types with and without rewards. Continuous-time Markov chains (CTMCs) are compared with (continuous-time) Markov reward models (MRMs) and generalized stochastic Petri nets (GSPNs) are compared with stochastic reward nets (SRNs). It is shown that reward-based models could lead to more concise model specification and solution of a variety of new measures. With respect to the solution of dependability and performability models, we identify three practical issues: largeness, stiffness, and non-exponentiality, and we discuss a variety of approaches to deal with them, including some of the latest research efforts.

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SURF A PROGRAM FOR DEPENDABILITY EVALUATlON OF COMPLEX FAULT-TOLERANT COMPUTING SYSTEMS

Cited by 12 publications

References 5 publications

A Simplified Method to Calculate Failure Times in Fault-Tolerant Systems

A Simplified Method to Calculate Failure Times in Fault-Tolerant Systems

Selecting and implementing phase approximations for semi-Markov models

Dependability and performability analysis

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