A Markov reward model checker

Katoen, Joost-Pieter; Khattri, M.; Zapreevt, I.S.

doi:10.1109/qest.2005.2

Cited by 185 publications

(141 citation statements)

References 7 publications

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“…Related topic include: probabilistic generalisations of bisimulation and simulation relations for DTMCs [49,57] and for CTMCs [17,11]; and approximate methods for stochastic model checking based on discrete event simulation [33,63]. Stochastic model checkers SMART [19], E T MC 2 [34] and MRMC [39] have similarities with the PRISM model checker described here. Finally, we mention a challenging direction of research is into the verification of models which allow more general probability distributions.…”

Section: Discussionmentioning

confidence: 99%

Stochastic Model Checking

Kwiatkowska

Norman

Parker

Lecture Notes in Computer Science

453

353

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Abstract. This tutorial presents an overview of model checking for both discrete and continuous-time Markov chains (DTMCs and CTMCs). Model checking algorithms are given for verifying DTMCs and CTMCs against specifications written in probabilistic extensions of temporal logic, including quantitative properties with rewards. Example properties include the probability that a fault occurs and the expected number of faults in a given time period. We also describe the practical application of stochastic model checking with the probabilistic model checker PRISM by outlining the main features supported by PRISM and three real-world case studies: a probabilistic security protocol, dynamic power management and a biological pathway.

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Section: Discussionmentioning

confidence: 99%

Stochastic Model Checking

Kwiatkowska

Norman

Parker

Lecture Notes in Computer Science

453

353

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“…We just mention PRISM [20], ETMCC [16] and M RM C [18]. However, many of these frameworks either do not use a process algebra to support system specification (like, for instance, MRMC and ETMCC), or the considered process algebra does not provide linguistic primitives for describing distribution and mobility.…”

Section: Discussionmentioning

confidence: 99%

“…This module, which implements the model checking algorithm proposed in [10], use an existing state-based stochastic modelchecker, the Markov Reward Model Checker (MRMC) [19], and wrapping it in the MoSL model-checking algorithm. After loading a StoKlaim specification and a MoSL formula, it verifies, by means of one or more calls to MRMC, the satisfaction of the formula by the specification.…”

Section: Sammentioning

confidence: 99%

Simulation and Analysis of Distributed Systems in Klaim

Calzolai¹,

Loreti²

2010

Lecture Notes in Computer Science

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Abstract. Network and distributed systems typically consists of a large number of actors that act and interact with each other in a highly dynamic environment. Due to the number of involved actors and their strong dependence on mobility and interaction, performance and dependability issues are of utmost importance for this class of systems. StoKlaim is a stochastic extension of Klaim specifically thought to facilitate the incorporation of random phenomena in models for networkaware computing. In this paper we show how StoKlaim can be used to specify and verify quantitative properties of distributed systems. To support the analysis an automatic tool is introduced and used.

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“…In order to be able to exploit available model checkers, transformation must finally provide input files for one of them. The two mostly established are PRISM [19,20] and MRMC [21]. The former exploits symbolic manipulation of PCTL properties in order to verify them on a compact representation of the state space; so it might be beneficial in case of complex formulae.…”

Section: Reliabilitymentioning

confidence: 99%

KlaperSuite: An Integrated Model-Driven Environment for Reliability and Performance Analysis of Component-Based Systems

Ciancone

Filieri

Drago

et al. 2011

Objects, Models, Components, Patterns

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Abstract. Automatic prediction tools play a key role in enabling the application of non-functional requirements analysis to selection and assembly of components for Component-Based Systems, reducing the need for strong mathematical skills to software designers. Exploiting the paradigm of Model Driven Engineering (MDE), it is possible to automate transformations from design models to analytical models, enabling for formal property verification. MDE is the core paradigm of KlaperSuite presented in this paper, which exploits the KLAPER pivot language to fill the gap between Design and Analysis of Component-Based Systems for reliability and performance properties. KlaperSuite is a family of tools empowering designers with the ability to capture and analyze QoS views of their systems by building a one-click bridge towards a number of established verification instruments.

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A Markov reward model checker

Abstract: Abstract

Cited by 185 publications

References 7 publications

Stochastic Model Checking

Stochastic Model Checking

Simulation and Analysis of Distributed Systems in Klaim

KlaperSuite: An Integrated Model-Driven Environment for Reliability and Performance Analysis of Component-Based Systems

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