A plug-in-based architecture for random number generation in simulation systems

Ewald,; Rossel,; Himmelspach,; Uhrmacher,

doi:10.1109/wsc.2008.4736147

Cited by 8 publications

(5 citation statements)

References 22 publications

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“…However, simulators are not monolithic entities; they depend on "helper" algorithms. For example, the influence of pseudorandom number generators on the quality of the results should not be neglected (Kelton 2000;Ewald, Rössel, Himmelspach, and Uhrmacher 2008). An additional helper might be the event queue implementation used, which, due to its central functionality, might have a significant influence on the overall simulation results if it does not behave as expected.…”

Section: Alternative Algorithmsmentioning

confidence: 99%

Component-Based Models and Simulations for Supporting Valid Multi-Agent System Simulations

Himmelspach

Röhl

Uhrmacher

2010

Applied Artificial Intelligence

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The more simulation becomes an established tool in the design of multi-agent systems, the more urgent the question becomes how valid the induced properties and behavior patterns are. Answering this question depends on the validity of the models, the correctness of the simulators, and the simulations. In all of these aspects, reuse and a declarative representation plays a crucial role. With James II, we developed a customizable framework for modeling and simulation. Its component-based architecture supports a reuse of models, simulators, and experimental settings. The benefits of this architecture for agent-based modeling and simulation will be illuminated by an excerpt of a simulation study about trading strategies for mobile ad hoc networks.

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Section: Alternative Algorithmsmentioning

confidence: 99%

Component-Based Models and Simulations for Supporting Valid Multi-Agent System Simulations

Himmelspach

Röhl

Uhrmacher

2010

Applied Artificial Intelligence

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“…Therefore, we created a flexible and extensible structure for implementing accordant simulation engines and components, The concrete components have been realized as plug-ins of james ii. For a further increase of flexibility, existing plug-ins facing general issues of simulation, e.g., random number generators [5] or event-queues [11] can be reused, extended, or exchanged as well. The opportunities to configure experiments in james ii like flexible instrumentation and observation of simulations, coarse-grained parallel execution, or dynamic calculation of required replications and simulation end times are applicable for experiments with the π-Calculus as well.…”

Section: Simulation Of the Pi-calculusmentioning

confidence: 99%

A flexible architecture for performance experiments with the pi-Calculus and its extensions

Leye

John

Uhrmacher

2010

Proceedings of the 3rd International ICST Conference on Simulation Tools and Techniques

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The π-Calculus is a modeling formalism for concurrent processes. Realized as part of the plug-in based modeling and simulation framework james ii, we propose an architecture for π-Calculus-based modeling and simulation, which supports both flexibility and efficiency. Facilitating the design of new π-Calculus-based formalisms and simulators is of particular relevance in the field of computational systems biology, for which many different π-Calculus dialects and simulators have been and still are being developed. Therefore, a flexible representation of π-Calculus models is used, which is illustrated by a mapping from the biochemical variant of the π-Calculus to the representation. Simulation engines are exchangeable and even automatically configurable according to the task at hand. Moreover, we present three different simulator implementations, working on the model representation. Efficiency denotes that our architecture supports the implementation of high-performance simulators. In order to assess efficiency, we perform experiments with these simulators and compare the results to the current cutting edge implementation in the field, the Stochastic Pi Machine.

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“…The question how to produce random numbers is not trivial. Many random generators produce random numbers of different "quality" [26,16]. They differ, e. g., with respect to correlation, fairness, and the length of the period.…”

Section: Some Problems Of Experimental Validationmentioning

confidence: 99%

A Discussion on Experimental Model Validation

Leye

Himmelspach²,

Uhrmacher³

2009

2009 11th International Conference on Computer Modelling and Simulation

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Model validation is essential in modeling and simulation. It "finalizes" the modeling process, and provides the base for reliable experiments with the model, and thus to gain trustworthy insights of the system under study. Diverse techniques have been developed addressing different needs and are used during different phases in the modeling and simulation life cycle. Experimental model validation depends on the execution of the model. Thus, the peculiarities of the simulation engine might influence the results of experiments and thus have to be taken into account. Execution also underlies some techniques of software validation which can be adopted for experimental model validation. New approaches apply model checking techniques for trace inspections, and emphasize the importance of an explicit description of requirements. All of this implies new requirements for systems intended to support experimental model validation.

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A plug-in-based architecture for random number generation in simulation systems

Cited by 8 publications

References 22 publications

Component-Based Models and Simulations for Supporting Valid Multi-Agent System Simulations

Component-Based Models and Simulations for Supporting Valid Multi-Agent System Simulations

A flexible architecture for performance experiments with the pi-Calculus and its extensions

A Discussion on Experimental Model Validation

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