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The usage of workflows to standardize processes, as well as to increase their efficiency and the quality of the results is a common technique. So far it has only been rarely applied in modeling and simulation. Herein we argue for employing this technique for the creation of various products in modeling and simulation. This includes the creation of models, simulations, modeling languages, and modeling...
Microsimulation is an increasingly popular tool in the social sciences. Individual behavior is described by a (commonly stochastic) model and subsequently simulated to study outcomes on the aggregate level. Demographic projections are a prominent area of application. Despite numerous available tools often new software is designed and implemented for specific applications. In this paper we describe...
The notion of logical processes is a widely used modeling paradigm in parallel and distributed discrete-event simulation. Yet, the comparison among different simulation algorithms for LP models still remains difficult. Most simulation systems only provide a small subset of available algorithms, which are usually selected and tuned towards specific applications. Furthermore, many modeling and simulation...
The development of M&S products often seems to be driven by need: people start coding because they are interested in either a concrete simulation study, or they are interested in a (single) research subject of M&S methodology. We claim that discussing, designing, developing, and comparing M&S products should be based on software engineering concepts. We shortly introduce some of these...
JAMES II is a general and open framework based on the ldquoPlug'n simulaterdquo concept, which enables developers to integrate their modeling and simulation methodological ideas into, and to create their applications upon an existing framework.This concept together with currently more than 400 plug-ins,and an explicit representation and storage of experiments ease developing modeling and simulation...
The increase and diversity of simulation methods bears witness of the need for more efficient discrete event simulations in computational biology-but how efficient are those methods, and how to ensure an efficient simulation for a concrete model? As the performance of simulation methods depends on the model, the simulator, and the infrastructure, general answers to those questions are likely to remain...
Numerous modeling and simulation software products have been developed in the last decades. Most of them have been created from scratch, often dedicated to single formalisms, single simulation algorithms, hardware platforms, or applications. But nevertheless each of these software products has to follow principles, and it has to contain techniques and elements to be usable for modeling and simulation...
Cellular automata are a well-known modeling formalism exploited in a wide range of application areas. In many of those, the complexity of models hampers a thorough analysis of the system under study. Therefore, efficient simulation algorithms are required. We present here a comparison of seven different simulation algorithms for cellular automata: the classical ldquofullrdquo simulator, the classical...
Model validation is essential in modeling and simulation. It ldquofinalizesrdquo 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...
Simulations often depend heavily on random numbers, yet the impact of random number generators is recognized seldom. The generation of random numbers for simulations is not trivial, as the quality of each algorithm depends on the simulation scenario. Therefore, simulation environments for large-scale experimentation with safety-critical models require a reliable mechanism to cope with this aspect...
Modeling and simulation frameworks for use in different application domains, throughout the complete development process, and in different hardware environments need to be highly scalable. For achieving an efficient execution, different simulation algorithms and data structures must be provided to compute a concrete model on a concrete platform efficiently. The support of parallel simulation techniques...
Stochastic simulations may require many replications until their results are statistically significant. Each replication corresponds to a standalone simulation job, so that these can be computed in parallel. This paper presents a grid-inspired approach to distribute such independent jobs over a set of computing resources that host simulation services, all of which are managed by a central master service...
No simulation algorithm will deliver best performance under all circumstances, so simulation systems often offer execution alternatives to choose from. This leads to another problem: how is the user supposed to know which algorithm to select? The need for an automated selection mechanism is often neglected, as many simulation systems are focused on specific applications or modeling formalisms and...
Efficiently simulating discrete-event models in a parallel and distributed manner is a challenging endeavour. On one hand, various factors, such as hardware infrastructure or model characteristics, have to be considered. On the other hand, there is a wide variety of algorithms which address subproblems of parallel and distributed simulation and whose performance depends on the application at hand...
BETA-BINDERS form a recently developed extension of stochastic \pi CALCULUS to describe micro-biological systems. It introduces special binders to wrap processes just as membranes enclose some living matter and hence to mimic biological interfaces. One means to define the operational semantics of a modeling formalism is by an abstract simulator description. In developing an abstract simulator for...
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