Integrated frameworks for multi-level and multi-formalism modeling

Sanders, William H.

doi:10.1109/pnpm.1999.796527

Cited by 29 publications

(14 citation statements)

References 26 publications

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“…The Möbius [7,9,10] tool can be seen as oriented to co-simulation, as it has been designed to build complex models by integrating submodels in different formalisms [24,25,26], but it requires the submodels to be developed with tools built-in in the Möbius framework. Another multi-formalism framework is SIMTHESys [14].…”

Section: Related Workmentioning

confidence: 99%

Towards Stochastic FMI Co-Simulations: Implementation of an FMU for a Stochastic Activity Networks Simulator

Bernardeschi

Domenici

Palmieri

2018

Software Technologies: Applications and Foundations

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The advantage of co-simulation with respect to traditional singleparadigm simulation lies mainly in the modeling flexibility it affords in composing large models out of submodels, each expressed in the most appropriate formalism. One aspect of this flexibility is the modularity of the co-simulation framework, which allows developers to replace each sub-model with a new version, possibly based on a different formalism or a different simulator, without changing the rest of the co-simulation. This paper reports on the replacement of a sub-model in a co-simulation built on the INTO-CPS framework. Namely, an existing co-simulation of a water tank, available in the INTO-CPS distribution, has been modified by replacing the tank sub-model with a sub-model built as a Stochastic Activity Network simulated on Möbius, a tool used to perform statistical analyses of systems with stochastic behavior. This work discusses aspects of this redesign, including the necessary modifications to the Möbius submodel. In this still preliminary work, the Stochastic Activity Network features related to stochastic models have not been used, but a simple deterministic model has proved useful in indicating an approach to the integration of Stochastic Activity Networks into a co-simulation framework.

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Section: Related Workmentioning

confidence: 99%

Towards Stochastic FMI Co-Simulations: Implementation of an FMU for a Stochastic Activity Networks Simulator

Bernardeschi

Domenici

Palmieri

2018

Software Technologies: Applications and Foundations

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“…To master complexity, a modeling methodology is needed so that only the relevant system aspects need to be detailed, allowing numerical results to be effectively computable. An excellent survey on the approaches dealing with model complexity (i.e., largeness avoidance and tolerance techniques) can be found in [17], while the available performability modeling tools and techniques are discussed in [8] and [19].…”

Section: Basic Principles and Related Workmentioning

confidence: 99%

An integrated framework for the dependability evaluation of distributed mobile applications

Kovács

Lollini

Majzik

et al. 2008

Proceedings of the 2008 RISE/EFTS Joint International Workshop on Software Engineering for Resilient Systems

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Current distributed mobile systems are usually characterized by a huge number of nodes, different network domains, different applications running, variability of the users' behavior, and dynamicity and heterogeneity of the communication networks. A typical example can be found in the automotive context, considering car-to-car and car-to-infrastructure communication scenarios. In this paper we propose a model-based approach for the dependability evaluation of distributed applications in a mobile environment. The final Multiple Phased System model representing the analyzed mobile scenario is automatically derived from highlevel UML specifications through a sequence of model transformation steps. The evaluation workflow is based on i) a hierarchical modelling approach that analyzes the system at different levels, namely communication, architecture, application and user level; ii) a time-based system decomposition identifying different phases for different user activities and environment conditions.

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“…After SHARPE, some other tools have been developed with the same goal. SMART [2], Möbius [4,10] and OsMoSys [12] are other multi-formalism modeling tools. Although the existing tools provide various capabilities, there are many difficulties to extend them to support new models.…”

Section: Introductionmentioning

confidence: 99%

PDETool: A Multi-formalism Modeling Tool for Discrete-Event Systems Based on SDES Description

Khalili

Bidgoly

Azgomi

2009

Applications and Theory of Petri Nets

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Abstract. Discrete-event systems have gained a lot of interest due to their wide range of applications. SDES is a unified description for stochastic discreteevent systems. The aim is to use this description as a basis of a new multiformalism modeling framework. In this paper, we introduce PDETool, which is based on SDES description. This modeling tool provides features for construction and translation of models into the XML-based input language of an SDES-based simulation engine that is developed for this purpose. Currently, we have implemented some useful extensions of Petri nets, such as stochastic Petri nets (SPNs), stochastic reward nets (SRNs) and stochastic activity networks (SANs) in this framework. PDETool is easily extensible to support a wide range of graphical and non-graphical formalisms. Furthermore, it facilitates the construction, animation and simulation of models. This tool has some advantages over the existing multi-formalism modeling and simulation tools, which will be mentioned in this paper.

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Integrated frameworks for multi-level and multi-formalism modeling

Cited by 29 publications

References 26 publications

Towards Stochastic FMI Co-Simulations: Implementation of an FMU for a Stochastic Activity Networks Simulator

Towards Stochastic FMI Co-Simulations: Implementation of an FMU for a Stochastic Activity Networks Simulator

An integrated framework for the dependability evaluation of distributed mobile applications

PDETool: A Multi-formalism Modeling Tool for Discrete-Event Systems Based on SDES Description

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