Computing k-out-of-n System Reliability

Barlow, Richard E.; Heidtmann, Klaus D.

doi:10.1109/tr.1984.5221843

Cited by 141 publications

(49 citation statements)

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“…It will be evident shortly that the number of non-permanent faults in an interval of particular length under the B fault model is a random variable, denoted by Υ , having Poisson Binomial distribution. The cumulative distribution function (CDF) of Υ can be computed based on the technique proposed by Barlow and Heidtmann (1984) to find the probability that there are at most S k,ρ non-permanent faults in an interval of length D k . And, 1 minus this probability is Pr(J E k > S k,ρ ), i.e., the probability that number of non-permanent faults in an interval of length D k is larger than S k,ρ .…”

Section: Probabilistic Schedulability Analysismentioning

confidence: 99%

Real-time scheduling algorithm for safety-critical systems on faulty multicore environments

Pathan

2016

Real-Time Syst

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An algorithm (called FTM) for scheduling of real-time sporadic tasks on a multicore platform is proposed. Each task has a deadline by which it must complete its non-erroneous execution. The FTM algorithm executes backups in order to recover from errors caused by non-permanent and permanent hardware faults. The worst-case schedulability analysis of FTM algorithm is presented considering an applicationlevel error model, which is independent of the stochastic behavior of the underlying hardware-level fault model. Then, the stochastic behavior of hardware-level fault model is plugged in to the analysis to derive the probability of meeting all the deadlines. Such probabilistic guarantee is the level of assurance (i.e., reliability) regarding the correct functional and timing behaviors of the system. One of the salient features of FTM algorithm is that it executes some backups in active redundancy to exploit the parallel multicore architecture while other backups passively to avoid unnecessary execution of too many active backups. This paper also proposes a scheme to determine for each task the number of backups that should run in active redundancy in order to increase the probability of meeting all the deadlines. The effectiveness of the proposed approach is demonstrated using an example application.

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Section: Probabilistic Schedulability Analysismentioning

confidence: 99%

Real-time scheduling algorithm for safety-critical systems on faulty multicore environments

Pathan

2016

Real-Time Syst

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“…However, more efficient recursive algorithms for reliability evaluation of such systems were reported by Barlow and Heidtmann [18] and Rushdi [19,20]. The iterative implementation Rushdi algorithm is provided in [21].…”

Section: K-out-of-n System With Iid Componentsmentioning

confidence: 99%

“…Each minimal path set contains exactly k different components and each minimal cut set contains exactly n − k + 1 components. If all minimal path sets and minimal cut sets are known, then the reliability of k-out-of-n system is calculated using the following formula [18]:…”

Section: K-out-of-n System With Iid Componentsmentioning

confidence: 99%

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Intuitionistic Fuzzy Reliability of k-out-of-n System using Statistical Confidence Interval

Kumar¹,

Bajaj²

2014

IJAIS

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In the present communication, some new arithmetic operations on intuitionistic fuzzy numbers using the α-cut method are introduced.A new methodology based on intuitionistic fuzzy confidence interval has been provided for analyzing the intuitionistic fuzzy system reliability of k-out-of-n system (particularly, series and parallel system), where the reliability of each component of each system is unknown. The reliability of each component of the system using the intuitionistic fuzzy statistical sample data using the α-cuts of (1 − γ)100% approach is estimated to compute the system reliability. Further, based on the estimated reliability of the components obtained, the intuitionistic fuzzy reliability of the system has been finally calculated using the minimal path sets approach.

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“…Applications of m-out-of-n systems can be found in various applied areas, for example, in safety monitoring, N version programming, etc. Therefore, a number of articles were devoted to algorithms and methods for reliability analysis of such the systems [2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Reliability models of m-out-of-n systems under incomplete information

Utkin¹

2004

Computers & Operations Research

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Computing k-out-of-n System Reliability

Cited by 141 publications

References 0 publications

Real-time scheduling algorithm for safety-critical systems on faulty multicore environments

Real-time scheduling algorithm for safety-critical systems on faulty multicore environments

Intuitionistic Fuzzy Reliability of k-out-of-n System using Statistical Confidence Interval

Reliability models of m-out-of-n systems under incomplete information

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