PARAM: A Model Checker for Parametric Markov Models

Hahn, Ernst Moritz; Hermanns, Holger; Wachter, Björn; Zhang, Lijun

doi:10.1007/978-3-642-14295-6_56

Cited by 92 publications

(84 citation statements)

References 12 publications

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“…Our implementation is written in such a way that it allows for rapid integration of future improvements of, e.g., the representation of regions or functions. The region-based method is a re-implementation of the tool PARAM 2.0 [6], [25]. It will be included in one of the forthcoming releases of PRISM.…”

Section: Prism Supportmentioning

confidence: 99%

Model Repair for Markov Decision Processes

Chen

Hahn

Han

et al. 2013

2013 International Symposium on Theoretical Aspects of Software Engineering

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Abstract-Markov decision processes (MDPs) are often used for modelling distributed systems with probabilistic failure or randomisation. We consider the problem of model repair for MDPs defined as follows: if the MDP fails to satisfy a property, we aim to find new values for the transition probabilities so that the property is guaranteed to hold, while at the same time the cost of repair is minimised. Because solving the MDP repair problem exactly is infeasible, in this paper we focus on approximate solution methods. We first formulate a region-based approach, which yields an interval in which the minimal repair cost is contained. As an alternative, we also consider samplingbased approaches, which are faster but unable to provide lower bounds on the repair cost. We have integrated both methods into the probabilistic model checker PRISM and demonstrated their usefulness in practice using a computer virus case study.

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Section: Prism Supportmentioning

confidence: 99%

Model Repair for Markov Decision Processes

Chen

Hahn

Han

et al. 2013

2013 International Symposium on Theoretical Aspects of Software Engineering

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“…Other statistical modeling tools, such as PRISM [17] and PARAM [16], have also been applied to model the domain of probabilistic scheduling systems. In [25] the authors compare three different approaches for re-sampling discrete probability distributions characterizing task execution times.…”

Section: Related Workmentioning

confidence: 99%

Flexible Framework for Statistical Schedulability Analysis of Probabilistic Sporadic Tasks

Boudjadar

Kim

David

et al. 2015

2015 IEEE 18th International Symposium on Real-Time Distributed Computing

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Abstract-The analysis of probabilistic schedulability explores all possible combinations of the probabilities of task attributes, which can easily lead to exponential computation time [24]. In this paper, we present a flexible schedulability analysis framework for periodic and sporadic tasks having probabilistic attributes where the computation time scales linearly in the size of analyzed systems. The framework is given in terms of a set of Parameterized Stopwatch Automata (PSA) models, which leads to a large degree of flexibility. Probability distributions for response time are generated using statistical model checking (UPPAAL SMC) while the overall schedulability can be checked using symbolic model checking (UPPAAL). We also define PoMD (percentage of missed deadlines) as a measure of the probabilistic schedulability of systems. To evaluate our approach, we compare the time used for computing response times and the analysis results using similar task models to that of a related analytical approach.

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“…In this section we give a brief overview of parametric Markov Chains [7,12,11]. First, we introduce some general notations.…”

Section: Parametric Markov Modelsmentioning

confidence: 99%

“…2. The time-unbounded reachability probability for the set of goal states G is calculated parametrically for the PMC generated in step 1 using the PARAM tool [11]. The result is a polynomial function.…”

Section: Algorithmmentioning

confidence: 99%

Time-Bounded Reachability in Distributed Input/Output Interactive Probabilistic Chains

Calin

Crouzen

D’Argenio

et al. 2010

Model Checking Software

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Abstract. We develop an algorithm to compute timed reachability probabilities for distributed models which are both probabilistic and nondeterministic. To obtain realistic results we consider the recently introduced class of (strongly) distributed schedulers, for which no analysis techniques are known. Our algorithm is based on reformulating the nondeterministic models as parametric ones, by interpreting scheduler decisions as parameters. We then apply the PARAM tool to extract the reachability probability as a polynomial function, which we optimize using nonlinear programming.

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PARAM: A Model Checker for Parametric Markov Models

Cited by 92 publications

References 12 publications

Model Repair for Markov Decision Processes

Model Repair for Markov Decision Processes

Flexible Framework for Statistical Schedulability Analysis of Probabilistic Sporadic Tasks

Time-Bounded Reachability in Distributed Input/Output Interactive Probabilistic Chains

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