OpenMOLE, a workflow engine specifically tailored for the distributed exploration of simulation models

Complex-systems describe multiple levels of collective structure and organization. In such systems, the emergence of global behaviour from local interactions is generally studied through large scale experiments on numerical models. This analysis generates important computation loads which require the use of multi-core servers, clusters or grid computing. Dealing with such large scale executions is especially challenging for modellers who do not possess the theoretical and methodological skills required to take advantage of high performance computing environments. That is why we have designed a cloud approach for model experimentation. This approach has been implemented in OpenMOLE (Open MOdeL Experiment) as a Domain Specific Language (DSL) that leverages the naturally parallel aspect of model experiments. The OpenMOLE DSL has been designed to explore user-supplied models. It delegates transparently their numerous executions to remote execution environment. From a user perspective, those environments are viewed as services providing computing power, therefore no technical detail is ever exposed. This paper presents the OpenMOLE DSL through the example of a toy model exploration and through the automated calibration of a real-world complex-system model in the field of geography.

[1]  Bruno Bachelet,et al.  Steady-state soil organic matter approximation model: application to the Pasture Simulation Model , 2012 .

[2]  Ryan K. Dale,et al.  CTCF-mediated transcriptional regulation through cell type-specific chromosome organization in the β-globin locus , 2012, Nucleic acids research.

[3]  Lena Sanders,et al.  Objets géographiques et simulation agent, entre thématique et méthodologie , 2007 .

[4]  David R. C. Hill,et al.  Parallel stochastic simulations with rigorous distribution of pseudo‐random numbers with DistMe: Application to life science simulations , 2012, Concurr. Comput. Pract. Exp..

[5]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[6]  Denise Pumain,et al.  Une approche de la complexité en géographie , 2003 .

[7]  Denise Pumain,et al.  Une théorie géographiqUe des villes , 2010 .

[8]  David R. C. Hill,et al.  Declarative task delegation in OpenMOLE , 2010, 2010 International Conference on High Performance Computing & Simulation.

[9]  J. I The Design of Experiments , 1936, Nature.

[10]  Giovanni Squillero,et al.  Bayesian Network Structure Learning from Limited Datasets through Graph Evolution , 2012, EuroGP.

[11]  Péter Kacsuk,et al.  P-GRADE Portal: A generic workflow system to support user communities , 2011, Future Gener. Comput. Syst..

[12]  Johan Montagnat,et al.  Flexible and Efficient Workflow Deployment of Data-Intensive Applications On Grids With MOTEUR , 2008, Int. J. High Perform. Comput. Appl..

[13]  Paul Bourgine,et al.  Optimal Viable Path Search for a Cheese Ripening Process using a Multi-objective EA , 2010, IJCCI.

[14]  Jack P. C. Kleijnen,et al.  Design Of Experiments: Overview , 2008, 2008 Winter Simulation Conference.

[15]  Ian J. Taylor,et al.  Visual Grid Workflow in Triana , 2005, Journal of Grid Computing.

[16]  Velimir V. Vesselinov,et al.  An agent-based approach to global uncertainty and sensitivity analysis , 2012, Comput. Geosci..

[17]  Robert G. Sargent,et al.  Verification and validation of simulation models , 2013, Proceedings of Winter Simulation Conference.

[18]  Romain Reuillon,et al.  Algorithmes évolutionnaires sur grille de calcul pour le calibrage de modéles géographiques , 2012 .

[19]  Alison J. Heppenstall,et al.  Genetic Algorithm Optimisation of An Agent-Based Model for Simulating a Retail Market , 2007 .

[20]  Ian J. Taylor,et al.  Workflows and e-Science: An overview of workflow system features and capabilities , 2009, Future Gener. Comput. Syst..

[21]  Bertram Ludäscher,et al.  Scientific workflow management and the Kepler system: Research Articles , 2006 .

[22]  Henrik Madsen,et al.  Automatic calibration of a conceptual rainfall-runoff model using multiple objectives. , 2000 .

[23]  John Holland,et al.  Adaptation in Natural and Artificial Sys-tems: An Introductory Analysis with Applications to Biology , 1975 .

[24]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

[25]  Michael Batty,et al.  Fifty Years of Urban Modeling: Macro-Statics to Micro-Dynamics , 2008 .

[26]  Yolanda Gil,et al.  Pegasus: Mapping Scientific Workflows onto the Grid , 2004, European Across Grids Conference.

[27]  Carole A. Goble,et al.  Taverna, Reloaded , 2010, SSDBM.

[28]  Denise Pumain,et al.  From theory to modelling: urban systems as complex systems , 2006 .

[29]  Jean-Pierre Aubin,et al.  Viability theory , 1991 .

[30]  Edward A. Lee,et al.  Scientific workflow management and the Kepler system , 2006, Concurr. Comput. Pract. Exp..

[31]  Anders Johansson,et al.  The Dynamics of Complex Urban Systems , 2008 .

[32]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[33]  Jean Sallantin,et al.  French Roadmap for complex Systems 2008-2009 , 2009 .

[34]  Sophie Martin,et al.  A viability approach to control food processes: Application to a Camembert cheese ripening process , 2012 .

[35]  Said Mirza Pahlevi,et al.  Editorial: A Special Issue from the Open Grid Forum , 2009 .