Towards Stochastic FMI Co-Simulations: Implementation of an FMU for a Stochastic Activity Networks Simulator

  • Cinzia Bernardeschi
  • Andrea Domenici
  • Maurizio Palmieri
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11176)


The advantage of co-simulation with respect to traditional single-paradigm simulation lies mainly in the modeling flexibility it affords in composing large models out of submodels, each expressed in the most appropriate formalism. One aspect of this flexibility is the modularity of the co-simulation framework, which allows developers to replace each sub-model with a new version, possibly based on a different formalism or a different simulator, without changing the rest of the co-simulation. This paper reports on the replacement of a sub-model in a co-simulation built on the INTO-CPS framework. Namely, an existing co-simulation of a water tank, available in the INTO-CPS distribution, has been modified by replacing the tank sub-model with a sub-model built as a Stochastic Activity Network simulated on Möbius, a tool used to perform statistical analyses of systems with stochastic behavior. This work discusses aspects of this redesign, including the necessary modifications to the Möbius sub-model. In this still preliminary work, the Stochastic Activity Network features related to stochastic models have not been used, but a simple deterministic model has proved useful in indicating an approach to the integration of Stochastic Activity Networks into a co-simulation framework.



The authors wish to thank the anonymous referees for their helpful suggestions.


  1. 1.
    Van der Auweraer, H., Anthonis, J., De Bruyne, S., Leuridan, J.: Virtual engineering at work: the challenges for designing mechatronic products. Eng. Comput. 29(3), 389–408 (2013). Scholar
  2. 2.
    Bernardeschi, C., Cassano, L., Domenici, A., Sterpone, L.: ASSESS: a simulator of soft errors in the configuration memory of SRAM-based FPGAs. IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 33(9), 1342–1355 (2014). Scholar
  3. 3.
    Bernardeschi, C., Cassano, L., Domenici, A.: Failure probability and fault observability of SRAM-FPGA systems. In: International Conference on Field Programmable Logic and Applications (FPL2011), pp. 385–388. IEEE, Sep 2011.
  4. 4.
    Bernardeschi, C., Domenici, A.: Verifying safety properties of a nonlinear control by interactive theorem proving with the Prototype Verification System. Inform. Process. Lett. 116(6), 409–415 (2016). Scholar
  5. 5.
    Bernardeschi, C., Domenici, A., Masci, P.: A PVS-simulink integrated environment for model-based analysis of cyber-physical systems. IEEE Trans. Softw. Eng. 44(6), 512–533 (2018). Scholar
  6. 6.
    Blochwitz, T., et al.: Functional mockup interface 2.0: the standard for tool independent exchange of simulation models. In: Proceedings of the 9th International MODELICA Conference, 3–5 September 2012, Munich, Germany, pp. 173–184. No. 76 in Linköping Electronic Conference Proceedings. Linköping University Electronic Press (2012).
  7. 7.
    Buchanan, C., Keefe, K.: Simulation debugging and visualization in the Möbius modeling framework. In: Norman, G., Sanders, W. (eds.) Quantitative Evaluation of Systems, pp. 226–240. Springer, Cham (2014). Scholar
  8. 8.
    Christiansen, M., Larsen, P., Nyholm Jørgensen, R.: Robotic design choice overview using co-simulation and design space exploration. Robotics 4, 398–421 (2015). Scholar
  9. 9.
    Clark, G., et al.: The Möbius modeling tool. In: 9th International Workshop on Petri Nets and Performance Models, pp. 241–250. IEEE Computer Society Press, Aachen, September 2001.
  10. 10.
    Deavours, D.D., et al.: The Möbius framework and its implementation. IEEE Trans. Softw. Eng. 28(10), 956–969 (2002). Scholar
  11. 11.
    Fitzgerald, J., Gamble, C., Larsen, P., Pierce, K., Woodcock, J.: Cyber-physical systems design: formal foundations, methods and integrated tool chains. In: Proceedings of the 2015 IEEE/ACM 3rd FME Workshop on Formal Methods in Software Engineering (FormaliSE), pp. 40–46. IEEE (2015).
  12. 12.
    Ford, M.D., Keefe, K., LeMay, E., Sanders, W.H., Muehrcke, C.: Implementing the ADVISE security modeling formalism in Möbius. In: 2013 43rd Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN), pp. 1–8, June 2013.
  13. 13.
    Gulati, R., Dugan, J.B.: A modular approach for analyzing static and dynamic fault trees. In: Annual Reliability and Maintainability Symposium, pp. 57–63. IEEE Computer Society Press (1997).
  14. 14.
    Iacono, M., Gribaudo, M.: Element based semantics in multi formalism performance models. In: 2010 IEEE International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems, pp. 413–416, August 2010.
  15. 15.
    Larsen, P.G., et al.: Integrated tool chain for model-based design of cyber-physical systems: the INTO-CPS project. In: 2016 2nd International Workshop on Modelling, Analysis, and Control of Complex CPS (CPS Data), pp. 1–6, April 2016.
  16. 16.
    Lawrence, D.P.Y., Gomes, C., Denil, J., Vangheluwe, H., Buchs, D.: Coupling Petri nets with deterministic formalisms using co-simulation. In: Proceedings of the Symposium on Theory of Modeling & Simulation, TMS-DEVS 2016, pp. 6:1–6:8. Society for Computer Simulation International, San Diego (2016).
  17. 17.
    Liu, J., Jiang, K., Wang, X., Cheng, B., Du, D.: Improved co-simulation with event detection for stochastic behaviors of CPSs. In: 2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC). vol. 1, pp. 209–214 , June 2016.
  18. 18.
    Mancini, T., Mari, F., Massini, A., Melatti, I., Merli, F., Tronci, E.: System level formal verification via model checking driven simulation. In: Sharygina, N., Veith, H. (eds.) CAV 2013. LNCS, vol. 8044, pp. 296–312. Springer, Heidelberg (2013). Scholar
  19. 19.
    Nelli, M., Bondavalli, A., Simoncini, L.: Dependability modeling and analysis of complex control systems: An application to railway interlocking. In: Hlawiczka, A., Silva, J.G., Simoncini, L. (eds.) EDCC 1996. LNCS, vol. 1150, pp. 91–110. Springer, Heidelberg (1996). Scholar
  20. 20.
    Oladimeji, P., Masci, P., Curzon, P., Thimbleby, H.: PVSio-web: a tool for rapid prototyping device user interfaces in PVS. In: FMIS2013, 5th International Workshop on Formal Methods for Interactive Systems, London, UK, 24 June 2013 (2013).
  21. 21.
    Owre, S., Rajan, S., Rushby, J.M., Shankar, N., Srivas, M.: PVS: Combining specification, proof checking, and model checking. In: Alur, R., Henzinger, T.A. (eds.) CAV 1996. LNCS, vol. 1102. Springer, Berlin (1996). Scholar
  22. 22.
    Palmieri, M., Bernardeschi, C., Masci, P.: Co-simulation of semi-autonomous systems: the line follower robot case study. In: Cerone, A., Roveri, M. (eds.) SEFM 2017. LNCS, vol. 10729, pp. 423–437. Springer, Cham (2018). Scholar
  23. 23.
    Payne, R., et al.: Examples Compendium 2. Tech. report D3.5, INTO-CPS Deliverable, December 2008Google Scholar
  24. 24.
    Peccoud, J., Courtney, T., Sanders, W.H.: Möbius: an integrated discrete-event modeling environment. Bioinformatics 23(24), 3412–3414 (2007). Scholar
  25. 25.
    Sanders, W.H.: Integrated frameworks for multi-level and multi-formalism modeling. In: Proceedings 8th International Workshop on Petri Nets and Performance Models (Cat. No.PR00331), pp. 2–9 (1999).
  26. 26.
    Sanders, W., Courtney, T., Deavours, D., Daly, D., Derisavi, S., Lam, V.: Multi-formalism and multi-solution-method modeling frameworks: The Möbius approach. In: Proceedings of Symposium on Performance Evaluation - Stories and Perspectives, Vienna, Austria, December 2003, pp. 241–256 (2003)Google Scholar
  27. 27.
    Sanders, W.H., Meyer, J.F.: Stochastic activity networks: formal definitions and concepts. In: Brinksma, E., Hermanns, H., Katoen, J.P. (eds.) EEF School 2000. LNCS, vol. 2090. Springer, Berlin (2001). Scholar
  28. 28.
    Srivastava, R., Peterson, M.S., Bentley, W.E.: Stochastic kinetic analysis of the Escherichia coli stress circuit using \(\sigma ^{32}\)-targeted antisense. Biotechnol. Bioeng. 75(1), 120–129 (2001). Scholar
  29. 29.
    Tsavachidou, D., Liebman, M.N.: Modeling and simulation of pathways in menopause. J. Am. Med. Inform. Assoc. 9(5), 461–471 (2002). Scholar
  30. 30.
    Vangheluwe, H.: Foundations of modelling and simulation of complex systems. Electronic Communications of the EASST 10 (2008).

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.DINFOUniversity of FlorenceFlorenceItaly
  2. 2.Department of Information EngineeringUniversity of PisaPisaItaly

Personalised recommendations