Towards a UML Profile for the Simulation Domain
Abstract
Model driven approaches have recently been exploited to implement simulation systems. Most of the reported contributions have adopted the Model Driven Architecture (MDA), a model driven approach widely used in software engineering. Simulation Platform Description Models (SPDM), which are first citizens MDA models intended for the description of simulation platforms supporting the execution of simulation experiments , are not explicitly considered in the previous works. The purpose of this work is to define a UML profile intended for the modeling of both simulation core concepts and simulation platforms. The contribution of this work is threefold: First we review and synthesize recent contributions in modeling and simulation approaches, practices and platforms; second we propose a resource-oriented approach for the modeling of simulation platform elements; third we consider both component- and workflow-based simulation platforms.
Full Text:
PDFReferences
A.-R. Da Silva, Model Driven Engineering: A Survey supportedby the Unified Conceptual Model, Computer languages, Systems and Structures, Vol. 43, pp. 139-155, Elsevier, 2015.
S. Wagner, D. Pfluger, and M. Mehl, Simulation Software Engineering: Experiences and Challenges, inProc.of the International Workshop on Software Engineering for High Performance Computing in Computational Science and Engineering, pp. 1-4, 2015.
O. Topcu, U. Durak, H. Oguztuzun, and L. Yilmaz, Distributed Simulation: A Model Driven Engineering Approach, Simulation Foundations, Methods and Applications, Springer, 2016.
G. Wagner,Model-Driven Engineering of Second-Life-Style Simulations, in the Proc. of the Winter simulation conference, 2010.
A. Yang, and W. Marquard, An Ontological Conceptualization of Multiscale Models, Computer ChemicalEngineering, Vol. 33, pp. 822-837, (2009).
J. Borgdorff, M. Mamonski, B. Bosak, K. Kurowski, M. Ben Belgacem, B, Chopard, D. Groen, P.-V. Covery, and A.-G. Hoekstra, Distributed Multiscale Computing with MUSCLE2, the Multiscale Coupling Library and Environment, Journal of Computational Science, Vol. 5, Issue. 5, pp. 719-731, Elsevier, 2014.
D. Cetinkaya, A. Verbraeck, A., and D.-M Seck,Applying a Model Driven Approach to Component Based Modeling and Simulation, in Proc. of the Winter Simulation conference, 2010.
OMG, A UML Profile for MARTE: Modeling and Analysis of Real-Time Embedded Systems, Beta 2, OMG Document Number: ptc/2008-06-09, 2008.
O. Babur, V. Smilauer, T. Verhoeff, and M.V-D. Brand,A survey of Open Source Multiphysics Frameworks in Engineering”, Procedisa Computer Science, Vol. 151, pp. 1088-1097, Elsevier, 2015.
O. Babur, T. Verhoeff, and M.G.-J. Van Den Brand, Multiphisics and Multiscale Sofware Frameworks: An Annotated Bibliography, Computer Science Reports, Technische University Eindhoven, Vol. 1501, 2015.
Y. Zaho, C. Jiang, and A. Yang, Towards Computer-Aided Multiscale Modeling: An Overarching Methodolgy and Support of Conceptual Modeling, Computer and Chemical Engineering, No.36, pp. 10-21, Elsevier, 2012.
M. Ben Belgacem, and al, Distributed Multiscale Computations Using the MAPPER Framework”,Procedia Computer Science, Vol. 13, pp. 1106-1115, Elsevier, 2013.
O. Hoenen, D. Coster, S. Petruczynik, and M. Plociennick,Coupled Simulations in Plasma Physics with the Integrated Plasma Simulator Platform, Procedia Computer Science, Vol. 5, pp. 1138-1147, Elsevier, 2015.
J. Borgdorff, J.-L. Falcone, E. Lorenz, C. Bona-Casas, B. Chopard, and A.-G. Hoekstr, Foundations of Distributed Multiscale Computing: Formalization, Specification and Analysis, Journal of Distributed Computing, Elsevier, Vol.73, pp. 465-483. 2013.
U. Yildez, A. Guabtni, and A.H-H. Ngu, Business versus Scientific workflow: A Comparative Study, Research Report No. 2009-3, Project DAKS, Department of Computer Science, UC Davis University of California, 2009.
T. Buchert, L. Nusbaum, and J. Gustedt, A Workflow-Inspired, Modular and Robust Approach to Experiments in Distributed Systems, Project-Team Algorille, Research Report n0 8404, Research Center Nancy-Grand Est, 2013.
C.-A. Ellis, Workflow Technology, Chapter No. 2 in Computer Supported Cooperative Work, Edited by Beaudouin-Lafon, John Wiley and Sons Ltd, 1999.
B. Dashtban, Scientific Workflow Patterns, Msc Dissertation, School of Advanced Computer Science , Manchester University, 2012.
X.-R. Xiang, andG. Madey, Improving the reuse of scientific workflows and their by-products, in Proc.IEEE International Conference on Web Services, pp. 792-799, 2007.
A. WeiB, andD. Karastoyanova,A Life Cycle for Coupled Multi-Scale, Multi-field Experiments Realized through Choreographies, in Proc. Enterprise Distributed Object Computing Conference (EDOC), 2014.
N. Cerezo, J. Montagnat, and M. Baly-Fornarino, Computer-Assisted Scientific Workflow Design, Journal of Grid Computing, Vol. 11, No. 3, pp. 585-610, Springer Verlag, 2013.
M. Maouche, M. Bettaz, Towards a Software Engineering Approach to Multi-Scale Modeling and Simulation, IJSEIA Journal, Vol. 10, 2016.
J. Qin, T. Fahringer, andS. Pllana, UML Based Grid Workflow Modeling Under ASKALON, Chapter in Distributed and Parallel Systems Book, pp. 191-200, Springer, 2007.
Garijo, D., Alper, P., Belhajjame, K., Corcho, O., Gi, Y., Common Motifs in Scientific Workflows: An Empirical Analysis, Future Generation Computer Systems, Elsevier, (2013).
R. Ferreira Da Silva, R., S. Camarasu-Pop, B. Grenier, V. Hamar, D. Manset, J. Montagnat, J. Revillard, J,-R. Balderrama, A. Tsaregorodtsev, and T. Glatad, Multi-Infrastructure Workflow Execution for Medical Simulation in the Virtual Imaging Platform, in Proc. HealthGrid Conference, pp.1-10, 2011.
D. Groen, S.-J. Zasade, and P.-V. Coveney, Survey of Multiscale and Multiphysics Applications and Communities, Computing in Science & Engineering, Vol. 16, Issue. 22, pp. 34-43, 2014.
DOI: https://doi.org/10.31449/inf.v43i1.1626
This work is licensed under a Creative Commons Attribution 3.0 License.