Advances in Petri nets 1986, part I on Petri nets: central models and their properties
Distributed and Parallel Databases
Workflow Management: Models, Methods, and Systems
Workflow Management: Models, Methods, and Systems
Scientific workflow management and the Kepler system: Research Articles
Concurrency and Computation: Practice & Experience - Workflow in Grid Systems
The simulation project life-cycle: models and realities
Proceedings of the 38th conference on Winter simulation
ANSS '07 Proceedings of the 40th Annual Simulation Symposium
Credible mobile ad hoc network simulation-based studies
Credible mobile ad hoc network simulation-based studies
The Trident Scientific Workflow Workbench
ESCIENCE '08 Proceedings of the 2008 Fourth IEEE International Conference on eScience
A flexible and scalable experimentation layer
Proceedings of the 40th Conference on Winter Simulation
Computational Biology and Chemistry
Enhancing the Scalability of Simulations by Embracing Multiple Levels of Parallelization
PDMC-HIBI '10 Proceedings of the 2010 Ninth International Workshop on Parallel and Distributed Methods in Verification, and Second International Workshop on High Performance Computational Systems Biology
Design considerations for M&S software
Winter Simulation Conference
WorMS- a framework to support workflows in M&S
Proceedings of the Winter Simulation Conference
Using workflows in M&S software
Proceedings of the Winter Simulation Conference
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To control the computation of an experiment means at least to deal with the generation of parameter combinations of interest (at best using experiment design solutions) and to execute, in case of stochastic simulations, the required replications using the hardware available. Although the process to be supported by modeling and simulation (M&S) software is often constrained to loading the model and instantiating it using a given parameter configuration and executing it by taking into account the simulation parameters, needs might arise to adapt and extend the code controlling the execution, i. e., incorporating feedback loops from validation or analysis steps into the execution scheme during runtime, having user interaction or simply adding further steps to the execution scheme such as further data processing. Additional features, like supporting diverse hardware setups, documentation or security, may imply further changes to the code. A workflow-based execution abstracts from concrete implementations and hard-coded execution patterns, as it provides a declarative description of this process, which means that anyone can set up own experimentation workflows (by using smaller predefined workflows and non-workflow-based components). Herein we present a workflow driven realization of an experimentation layer, which supports the same features as the hard-coded alternative and we discuss the pros, cons, and performance of the workflow-based approach.