Reliability and Availability Analysis of Self-stabilizing Systems

Dhama, Abhishek; Theel, Oliver; Warns, Timo

doi:10.1007/978-3-540-49823-0_17

Cited by 6 publications

(4 citation statements)

References 11 publications

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“…This motivates an effort to instead quantitatively analyze distributed algorithms [21], [9], [18], [17]. Instead of proving that an algorithm works, quantitative analysis aims at studying how well an algorithm works.…”

Section: Introductionmentioning

confidence: 99%

“…Instead of assuming that there only occur a limited number of faults, or that faults occur only in a certain time-period, we now assume that transient faults can occur at any time, but with a specific probability. This allows to calculate probability bounds on the correct operation of the algorithm [18], [17] provided fault probabilities are given.…”

Section: Introductionmentioning

confidence: 99%

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Bounded Fairness for Probabilistic Distributed Algorithms

Crouzen

Hahn

Hermanns

et al. 2011

2011 Eleventh International Conference on Application of Concurrency to System Design

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This paper investigates quantitative dependability metrics for distributed algorithms operating in the presence of sporadic or frequently occurring faults. In particular, we investigate necessary revisions of traditional fairness assumptions in order to arrive at useful metrics, without adding hidden assumptions that may obfuscate their validity. We formulate faulty distributed algorithms as Markov decision processes to incorporate both probabilistic faults and non-determinism arising from concurrent execution. We lift the notion of bounded fairness to the setting of Markov decision processes. Bounded fairness is particularly suited for distributed algorithms running on nearly symmetric infrastructure, as it is common for sensor network applications. Finally, we apply this fairness notion in the quantitative model-checking of several case studies.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bounded Fairness for Probabilistic Distributed Algorithms

Crouzen

Hahn

Hermanns

et al. 2011

2011 Eleventh International Conference on Application of Concurrency to System Design

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“…That is, faults may be intermittent and the temporal separation between them, at times, may not be large enough to allow the system to converge. In this context reliability, instantaneous availability, and limiting availability have been defined for self-stabilizing systems in [1]. An important part of these definitions is the notion of a system doing "something useful."…”

Section: Dependability Metricsmentioning

confidence: 99%

“…Based on the evaluation of relevant dependability metrics, a decision should be taken of which solution out of the set of present solutions should be chosen and put to work for ones purposes. By building on [1], we present useful dependability metrics for differentiating among self-stabilizing solutions and show how to evaluate them. For this purpose, we propose the modeling of a self-stabilizing algorithm together with the assumed fault model in terms of a discrete-time Markov decision process or a discrete-time Markov chain.…”

Section: Introductionmentioning

confidence: 99%

Dependability Engineering of Silent Self-stabilizing Systems

Dhama

Theel

Crouzen

et al. 2009

Lecture Notes in Computer Science

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Abstract. Self-stabilization is an elegant way of realizing non-masking fault-tolerant systems. Sustained research over last decades has produced multiple self-stabilizing algorithms for many problems in distributed computing. In this paper, we present a framework to evaluate multiple selfstabilizing solutions under a fault model that allows intermittent transient faults. To that end, metrics to quantify the dependability of selfstabilizing systems are defined. It is also shown how to derive models that are suitable for probabilistic model checking in order to determine those dependability metrics. A heuristics-based method is presented to analyze counterexamples returned by a probabilistic model checker in case the system under investigation does not exhibit the desired degree of dependability. Based on the analysis, the self-stabilizing algorithm is subsequently refined.

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