Fluid Model Checking

Bortolussi, Luca; Hillston, Jane

doi:10.1007/978-3-642-32940-1_24

Cited by 47 publications

(115 citation statements)

References 52 publications

(117 reference statements)

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“…From Table II, we see that the obtained reward converges to a fixed value with increasing model parameter N. Therefore, if one is interested in the value obtained if the model parameters go towards infinity, methods concerned with the limiting behaviour [7,8,40, 1] might be more appropriate. As discussed in the introduction, our method targets at finding conservative bounds for the model under consideration.…”

Section: Case Studiesmentioning

confidence: 99%

Transient Reward Approximation for Continuous-Time Markov Chains

et al. 2015

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We are interested in the analysis of very large continuoustime Markov chains (CTMCs) with many distinct rates. Such models arise naturally in the context of reliability analysis, e. g., of computer network performability analysis, of power grids, of computer virus vulnerability, and in the study of crowd dynamics. We use abstraction techniques together with novel algorithms for the computation of bounds on the expected final and accumulated rewards in continuous-time Markov decision processes (CTMDPs). These ingredients are combined in a partly symbolic and partly explicit (symblicit) analysis approach. In particular, we circumvent the use of multi-terminal decision diagrams, because the latter do not work well if facing a large number of different rates. We demonstrate the practical applicability and efficiency of the approach on two case studies.

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Section: Case Studiesmentioning

confidence: 99%

Transient Reward Approximation for Continuous-Time Markov Chains

et al. 2015

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“…It is either an atomic target or a pair of simpler targets combined using the standard logic operators ∧ and ∨. An atomic target f (pv ,sn) is a triple denoting the application of a matching function f to policy values 9 pv from the policy and to policy values from the evaluation context identified by attribute (structured) names 10 sn. In fact, an attribute name refers to a specific attribute of the request or of the environment, which is available via the evaluation context.…”

Section: Policies and Their Syntaxmentioning

confidence: 99%

“…In practice, one can be interested in modelling also the event of failed delivery of the observers. This is interesting both for producing more realistic models (with unreliable network communication), and for allowing the application of advanced analysis techniques based on fluid approximation [10], such as fluid model-checking [9]. Therefore, we add an error probability to the observers delivery, which we denoted p err (or simply err, in Fig.…”

Section: Stocs: Stochastic Scelmentioning

confidence: 99%

The SCEL Language: Design, Implementation, Verification

Nicola

Latella

Lafuente

et al. 2015

Software Engineering for Collective Autonomic Systems

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Abstract. SCEL (Service Component Ensemble Language) is a new language specifically designed to rigorously model and program autonomic components and their interaction, while supporting formal reasoning on their behaviors. SCEL brings together various programming abstractions that allow one to directly represent aggregations, behaviors and knowledge according to specific policies. It also naturally supports programming interaction, self-awareness, context-awareness, and adaptation. The solid semantic grounds of the language is exploited for developing logics, tools and methodologies for formal reasoning on system behavior to establish qualitative and quantitative properties of both the individual components and the overall systems.

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“…We close this section showing the application of the mean field model-checker FlyFast [19] on the foraging ants example. Fluid model-checking techniques have recently been proposed as scalable techniques for the verification of properties of one (or a few) agents in the context of large populations [4]. These techniques are based on differential semantics, or on difference equations, when considering their discrete time counterparts, as is the case for FlyFast.…”

Section: Mean-field Modelmentioning

confidence: 99%

Investigating Fluid-Flow Semantics of Asynchronous Tuple-Based Process Languages for Collective Adaptive Systems

Latella

Loreti

Massink

2015

Lecture Notes in Computer Science

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Abstract. Recently, there has been growing interest in nature-inspired interaction paradigms for Collective Adaptive Systems, for modelling and implementation of adaptive and context-aware coordination, among which the promising pheromone-based interaction paradigm. System modelling in the context of such a paradigm may be facilitated by the use of languages in which adaptive interaction is decoupled in time and space through asynchronous buffered communication, e.g. asynchronous, repository-or tuple-based languages. In this paper we propose a differential semantics for such languages. In particular, we consider an asynchronous, repository based modelling kernel-language which is a restricted version of LINDA, extended with stochastic information about action duration. We provide stochastic formal semantics for both an agent-based view and a population-based view. We then derive an ordinary differential equation semantics from the latter, which provides a fluid-flow deterministic approximation for the mean behaviour of large populations. We show the application of the language and the ODE analysis on a benchmark example of foraging ants.

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Fluid Model Checking

Cited by 47 publications

References 52 publications

Transient Reward Approximation for Continuous-Time Markov Chains

Transient Reward Approximation for Continuous-Time Markov Chains

The SCEL Language: Design, Implementation, Verification

Investigating Fluid-Flow Semantics of Asynchronous Tuple-Based Process Languages for Collective Adaptive Systems

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