Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.5555/2263019.2263048acmotherconferencesArticle/Chapter ViewAbstractPublication PagessimutoolsConference Proceedingsconference-collections
research-article

JAMES II: extending, using, and experiments

Published: 19 March 2012 Publication History

Abstract

JAMES II is a modeling and simulation framework designed to ease the creation of specialized M&S applications, to experiment with models, and to ease the experimentation with alternative data structures and computation algorithms. The flexibility of the framework is based on the plug'n simulate architecture which allows to have any number of alternatives coexisting, from modeling means over computation algorithms to experiment control and analysis, within a single software. Herein we show how alternatives are added to JAMES II, how JAMES II can be used to efficiently execute simulations, what can be reused to built specialized M&S software, and how this can be used to do a more fair comparison of the alternatives.

References

[1]
R. Ewald. Automatic Algorithm Selection for Complex Simulation Problems. Phd thesis, Faculty of Computer Science and Electrical Engineering, University of Rostock, Vieweg+Teubner, 2011.
[2]
R. Ewald, J. Himmelspach, and A. M. Uhrmacher. An algorithm selection approach for simulation systems. In Proceedings of the PADS, pages 91--98, Rome, Italy, June 2008. IEEE Computer Society.
[3]
J. Himmelspach, R. Ewald, S. Leye, and A. M. Uhrmacher. Enhancing the scalability of simulations by embracing multiple levels of parallelization. In Proceedings of the 2010 International Workshop on High Performance Computational Systems Biology. IEEE CPS, Sept. 2010.
[4]
J. Himmelspach, R. Ewald, and A. M. Uhrmacher. A flexible and scalable experimentation layer for JAMES II. In S. Mason, R. Hill, L. Moench, and O. Rose, editors, Proceedings of the Winter simulation conference, pages 827--835. Winter Simulation Conference, Dec. 2008.
[5]
J. Himmelspach, M. Röhl, and A. M. Uhrmacher. Component based modelling and simulation for valid multi-agent system simulations. International journal for Applied Artificial Intelligence, 24(5):414--442, May 2010.
[6]
J. Himmelspach and A. M. Uhrmacher. Sequential processing of PDEVS models. In A. G. Bruzzone, A. Guasch, M. A. Piera, and J. Rozenblit, editors, Proceedings of the 3rd EMSS, pages 239--244, Barcelona, Spain, Oct. 2006. Piera, LogiSim.
[7]
J. Himmelspach and A. M. Uhrmacher. The event queue problem and PDEVS. In Proceedings of the DEVS Integrated M&S Symposium, pages 257--264, Norfolk, VA, Mar. 2007. SCS.
[8]
J. Himmelspach and A. M. Uhrmacher. Plug'n simulate. In ANSS '07: Proceedings of the 40th Annual Simulation Symposium, pages 137--143, Washington, DC, USA, Mar. 2007. IEEE Computer Society.
[9]
M. Jeschke. Efficient Non-Spatial and Spatial Simulation of Biochemical Reaction Networks. PhD thesis, Universität Rostock, Rostock, 2010.
[10]
M. John. Reaction constraints for the pi-calculus: a language for the stochastic and spatial modeling of cell-biological processes. Logos Verlag, Berlin, 2011.
[11]
D. Johnson. A theoretician's guide to the experimental analysis of algorithms. In Fifth and Sixth DIMACS Implentation Challenges, 2002.
[12]
S. Leye, J. Himmelspach, M. Jeschke, R. Ewald, and A. M. Uhrmacher. A grid-inspired mechanism for coarse-grained experiment execution. In D. Roberts, A. E. Saddik, and A. Ferscha, editors, Proceedings of the 12th IEEE International Symposium on Distributed Simulation and Real-Time Applications, pages 7--16, Los Alamitos, CA, USA, Oct. 2008. IEEE Computer Society. DSRT 08 (Vancouver, British Columbia, Canada).
[13]
C. Maus, S. Rybacki, and A. M. Uhrmacher. Rule-based multi-level modeling of cell biological systems. BMC Systems Biology, 5(166), 2011.
[14]
O. Mazemondet. Spatio-temporal Dynamics of the Wnt/β-catenin Signaling Pathway: A Computational Systems Biology Approach. PhD thesis, University of Rostock, Rostock, 2011.
[15]
Z. Merali.... why scientific programming does not compute. Nature, 467:775--777, Oct. 2010.
[16]
N. Minar, R. Burkhart, C. Langton, and M. Askenazi. The SWARM simulation system: A toolkit for building multi-agent simulations. Technical report, Santa Fe Institute, Juni 1996.
[17]
M. Oertel, J. Himmelspach, and A. Martens. Teaching and training system plus modeling and simulation -- a plugin based approach. In Proceedings of the Tenth International Conference on Computer Modeling and Simulation, pages 475--480, Los Alamitos, CA, USA, 2008. IEEE Computer Society.
[18]
K. Perumalla. μsik: a micro-kernel for parallel/distributed simulation systems. In ACM/IEEE/SCS Workshop on Parallel and Distributed Simulation (PADS), pages 59--68, Monterey, CA, Juni 2005. IEEE Computer Society Press.
[19]
M. Röhl, B. König-Ries, and A. M. Uhrmacher. An experimental frame for evaluating service trading in mobile ad-hoc networks. In Mobilität und Mobile Informationssysteme (MMS 2007), volume 104 of Lect. Notes Inform., pages 37--48, 2007.
[20]
S. Rybacki, J. Himmelspach, and A. M. Uhrmacher. Cpu and gpu based simulation of cellular automata - a performance comparison. In Proceedings of the 1st SIMUL, pages 62--67. IEEE Computer Society, Sept. 2009.
[21]
S. Rybacki, J. Himmelspach, and A. M. Uhrmacher. Worms- a framework to support workflows in m&s. In S. Jain, R. R. Creasey, J. Himmelspach, K. P. White, and M. Fu, editors, Proceedings of the 2011 Winter Simulation Conference, Piscataway, New Jersey, 2011. Institute of Electrical and Electronics Engineers, Inc.
[22]
A. M. Uhrmacher, R. Ewald, J. Himmelspach, M. Jeschke, M. John, S. Leye, C. Maus, and M. Röhl. One modelling formalism & simulator is not enough! -- A perspective for computational biology based on JAMES II. In J. Fisher, editor, Proceedings of the the 1st FMSB Workshop, number 5054 in LNBI, pages 123--138, Cambridge, UK, June 2008. Springer.
[23]
A. M. Uhrmacher, J. Himmelspach, and R. Ewald. Discrete-Event Modeling and Simulation: Theory and Applications, chapter Effective and efficient modeling and simulation of DEVS variants, pages 139--176. Taylor and Francis, Dec. 2010.
[24]
S. Zinn. A Continuous-Time Microsimulation and First Steps Towards a Multi-Level Approach in Demography. PhD thesis, University of Rostock, Rostock, 2011.
[25]
S. Zinn, J. Himmelspach, J. Gampe, and A. M. Uhrmacher. Miccore: A tool for microsimulation. In R. R. H. M. D. Rossetti and, B. Johansson, A. Dunkin, and R. G. Ingalls, editors, Proceedings of the 2009 Winter Simulation Conference, pages 992--1002. Winter Simulation Conference, Dec. 2009.

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
SIMUTOOLS '12: Proceedings of the 5th International ICST Conference on Simulation Tools and Techniques
March 2012
402 pages
ISBN:9781450315104

In-Cooperation

Publisher

ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)

Brussels, Belgium

Publication History

Published: 19 March 2012

Check for updates

Author Tags

  1. experimentation
  2. framework
  3. performance comparison
  4. plug-ins
  5. reuse
  6. software

Qualifiers

  • Research-article

Conference

SIMUTOOLS'12

Acceptance Rates

Overall Acceptance Rate 20 of 73 submissions, 27%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 72
    Total Downloads
  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 08 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media