Simulation Debugging and Visualization in the Möbius Modeling Framework

  • Craig Buchanan
  • Ken Keefe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8657)


Large and complex models can be difficult to analyze using static analysis results from current tools, including the Möbius modeling framework, which provides a powerful, formalism-independent, discrete-event simulator that outputs static results such as execution traces. The Möbius Simulation Debugger and Visualization (MSDV) feature adds user interaction to running simulations to provide a more transparent view into the dynamics of the models under consideration. This paper discusses the details of the design and implementation of the feature in the Möbius modeling environment. Also, a case study is presented to demonstrate the new abilities provided by the feature.


discrete-event simulation simulation visualization model debugging multi-formalism modeling 


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  1. 1.
    Jazouli, T., Sandborn, P., Kashani-Pour, A.: A Direct Method for Determining Design and Support Parameters to Meet an Availability Requirement. International Journal of Performability Engineering 10(2), Paper 09, 211–225 (2014)Google Scholar
  2. 2.
    Andersson, J.: Environmental Impact Assessment using Production Flow Simulation. Research series from Chalmers University of Technology, Department of Product and Production Development: report, ISSN 1652-9243; nr 85Google Scholar
  3. 3.
    Viana, J., Brailsford, S.C., Harindra, V., Harper, P.R.: Combining Discrete-event Simulation and System Dynamics in a Healthcare Setting: A Composite Model for Chlamydia Infection. European Journal of Operational Research (March 2014)Google Scholar
  4. 4.
    Clark, G., Courtney, T., Daly, D., Deavours, D., Derisavi, S., Doyle, J.M., Sanders, W.H., Webster, P.: The Möbius Modeling Tool. In: Proceedings of the 9th International Workshop on Petri Nets and Performance Models, Aachen, Germany, September 11-14, pp. 241–250 (2001)Google Scholar
  5. 5.
  6. 6.
  7. 7.
    Williamson, A.L.: Discrete Event Simulation in the Möbius Modeling Framework. Master’s Thesis, University of Illinois at Urbana-Champaign (1998)Google Scholar
  8. 8.
    Kuratti, A.: Improved Techniques for Parallel Discrete Event Simulation. Ph.D. Thesis, University of Illinois at Urbana-Champaign (1997)Google Scholar
  9. 9.
  10. 10.
  11. 11.
  12. 12.
    Derisavi, S., Kemper, P., Sanders, W.H., Courtney, T.: The Möbius State-Level Abstract Functional Interface. In: Field, T., Harrison, P.G., Bradley, J., Harder, U. (eds.) TOOLS 2002. LNCS, vol. 2324, pp. 31–50. Springer, Heidelberg (2002)Google Scholar
  13. 13.
    Deavours, D.: Formal Specification of the Möbius Modeling Framework. Doctoral Dissertation, University of Illinois at Urbana-Champaign (2001)Google Scholar
  14. 14.
    Sanders, W.H., Meyer, J.F.: Stochastic Activity Networks: Formal Definitions and Concepts. In: Brinksma, E., Hermanns, H., Katoen, J.-P. (eds.) FMPA 2000. LNCS, vol. 2090, pp. 315–343. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  15. 15.
    Berthier, R., Jetcheva, J., Mashima, D., Huh, J., Grochocki, D., Bobba, R., Cárdenas, A., Sanders, W.: Reconciling Security Protection and Monitoring Requirements in Advanced Metering Infrastructures. In: Proceedings of the IEEE International Conference on Smart Grid Communications (SmartGridComm), Vancouver, Canada, October 21-24 (2013)Google Scholar
  16. 16.
    Berthier, R., Sanders, W.H., Khurana, H.: Intrusion Detection for Advanced Metering Infrastructures: Requirements and Architectural Directions. In: Proceedings of the 1st IEEE International Conference on Smart Grid Communications (SmartGridComm), Gaithersburg, Maryland, October 4-6, pp. 350–355 (2010)Google Scholar
  17. 17.
    Cárdenas, A., Berthier, R., Bobba, R., Huh, J., Jetcheva, J., Grochocki, D., Sanders, W.H.: A Framework for Evaluating Intrusion Detection Architectures in Advanced Metering Infrastructures. IEEE Transactions on Smart GridGoogle Scholar
  18. 18.
    Grochocki, D., Huh, J., Berthier, R., Bobba, R., Cárdenas, A., Jetcheva, J., Sanders, W.H.: AMI Threats, Intrusion Detection Requirements and Deployment Recommendations. In: Proceedings of the 3rd IEEE International Conference on Smart Grid Communications (SmartGridComm), Tainan City, Taiwan, November 5-8, pp. 395–400 (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Craig Buchanan
    • 1
  • Ken Keefe
    • 1
  1. 1.Information Trust InstituteU.S.A.

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