Skip to main content
Log in

Domain-specific discrete event modelling and simulation using graph transformation

  • Theme Section Paper
  • Published:
Software & Systems Modeling Aims and scope Submit manuscript

Abstract

Graph transformation is being increasingly used to express the semantics of domain-specific visual languages since its graphical nature makes rules intuitive. However, many application domains require an explicit handling of time to accurately represent the behaviour of a real system and to obtain useful simulation metrics to measure throughputs, utilization times and average delays. Inspired by the vast knowledge and experience accumulated by the discrete event simulation community, we propose a novel way of adding explicit time to graph transformation rules. In particular, we take the event scheduling discrete simulation world view and provide rules with the ability to schedule the occurrence of other rules in the future. Hence, our work combines standard, efficient techniques for discrete event simulation (based on the handling of a future event set) and the intuitive, visual nature of graph transformation. Moreover, we show how our formalism can be used to give semantics to other timed approaches and provide an implementation on top of the rewriting logic system Maude.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. AnyLogic. http://www.xjtek.com/

  2. Balasubramanian, D., Narayanan, A., van Buskirk, C., Karsai, G.: The graph rewriting and transformation language: GReAT. ECEASST, 1, 2006. http://www.isis.vanderbilt.edu/tools/GReAT

  3. Bohl M., Rynn M.: Tools for Structured and Object-Oriented Design, 7th edn. Prentice Hall, Upper Saddle River (2007)

    Google Scholar 

  4. Boronat, A., Heckel, R., Meseguer, J.: Rewriting logic semantics and verification of model transformations. In: FASE’09. LNCS, vol. 5503, pp. 18–33. Springer, Berlin (2009)

  5. Boronat A., Meseguer J.: An algebraic semantics for MOF. Formal Asp. Comput. 22, 269–296 (2010)

    Article  MATH  Google Scholar 

  6. Boronat, A., Ölveczky, P.C.: Formal real-time model transformations in MOMENT2. In: FASE’10. LNCS, vol. 6013, pp. 29–43. Springer, Berlin (2010)

  7. Cassandras C.G., Lafortune S.: Introduction to Discrete Event Systems, 2nd edn. Springer, Berlin (2008)

    Book  MATH  Google Scholar 

  8. Clavel, M., Durán, F., Eker, S., Lincoln, P., Martí-Oliet, N., Meseguer, J., Talcott, C.L. (eds.): All about Maude—a high-performance logical framework, how to specify, program and verify systems in rewriting logic. LNCS, vol. 4350. Springer, Berlin (2007). http://maude.cs.uiuc.edu

  9. de Lara J.: Meta-modelling and graph transformation for the simulation of systems. Bull. EATCS 81, 180–194 (2003)

    Google Scholar 

  10. de Lara J., Bardohl R., Ehrig H., Ehrig K., Prange U., Taentzer G.: Attributed graph transformation with node type inheritance. Theor. Comput. Sci. 376(3), 139–163 (2007)

    Article  MATH  Google Scholar 

  11. de Lara, J., Guerra, E., Boronat, A., Heckel, R., Torrini, P.: Graph transformation for domain-specific discrete event time simulation. In: ICGT’10. LNCS, vol. 6372, pp. 266–281. Springer, Berlin (2010)

  12. de Lara J., Vangheluwe H.: Automating the transformation-based analysis of visual languages. Formal Asp. Comput. 22(3–4), 297–326 (2010)

    Article  MATH  Google Scholar 

  13. Eckardt, T., Heinzemann, C., Henkler, S., Hirsch, M., Priesterjahn, C., Schäfer, W.: Modeling and verifying dynamic communication structures based on graph transformations. Comput. Sci. Res. Dev. (2011, in press)

  14. Edwards S.A., Lee E.A.: The semantics and execution of a synchronous block-diagram language. Sci. Comput. Program. 48(1), 21–42 (2003)

    Article  MATH  MathSciNet  Google Scholar 

  15. Ehrig H., Ehrig K., Prange U., Taentzer G.: Fundamentals of Algebraic Graph Transformation. Springer, Berlin (2006)

    MATH  Google Scholar 

  16. Eker J., Janneck J., Lee E., Liu J., Liu X., Ludvig J., Neuendorffer S., Sachs S., Xiong Y.: Taming heterogeneity—the ptolemy approach. Proc. IEEE 91(1), 127–144 (2003)

    Article  Google Scholar 

  17. Ermel, C., Biermann, E., Schmidt, J., Warning, A.: Visual modeling of controlled emf model transformation using henshin. ECEASST, 32 (2010)

  18. ExtendSim. http://www.extendsim.com

  19. Fischer, T., Niere, J., Torunski, L., Zündorf, A.: Story diagrams: A new graph rewrite language based on the unified modeling language and java. In: TAGT. LNCS, vol. 1764, pp. 296–309. Springer, Berlin (2000). http://www.fujaba.de

  20. Fishman G.S.: Discrete-Event Simulation: Modeling, Programming, and Analysis. Springer, Berlin (2001)

    Book  Google Scholar 

  21. Gönczy L., Kovács M., Varró D.: Modeling and verification of reliable messaging by graph transformation systems. ENTCS 175(4), 37–50 (2007)

    Google Scholar 

  22. Gyapay S., Varró D., Heckel R.: Graph transformation with time. Fundam. Inf. 58(1), 1–22 (2003)

    MATH  Google Scholar 

  23. Heckel R., Lajios G., Menge S.: Stochastic graph transformation systems. Fundam. Inf. 74(1), 63–84 (2006)

    MATH  MathSciNet  Google Scholar 

  24. Kelly S., Tolvanen J.-P.: Domain-Specific Modeling. Enabling Full Code Generation. Wiley-IEEE CS, New York (2008)

    Book  Google Scholar 

  25. Lambers, L., Ehrig, H., Orejas, F.: Conflict detection for graph transformation with negative application conditions. In: ICGT’06. LNCS, vol. 4178, pp. 61–76. Springer, Berlin (2006)

  26. Lee, E.A., Zheng, H.: Leveraging synchronous language principles for heterogeneous modeling and design of embedded systems. In: EMSOFT, pp. 114–123. ACM, New York (2007)

  27. Lengyel, L., Levendovszky, T., Mezei, G., Charaf, H.: A visual control flow language for model transformation systems. In: IASTED Conf. on Software Engineering, pp. 194–199. IASTED/ACTA Press (2006). http://avalon.aut.bme.hu/~tihamer/research/vmts/

  28. Marsan M.A., Balbo G., Conte G., Donatelli S., Franceschinis G.: Modelling with Generalized Stochastic Petri Nets. Wiley, New York (1995)

    MATH  Google Scholar 

  29. Mathaikutty D.A., Patel H.D., Shukla S.K., Jantsch A.: SML-Sys: a functional framework with multiple models of computation for modeling heterogeneous system. Des. Autom. Embed. Syst. 12, 1–30 (2008)

    Article  Google Scholar 

  30. Naeem, M., Heckel, R., Orejas, F., Hermann, F.: Incremental service composition based on partial matching of visual contracts. In: FASE’10. LNCS, vol. 6013, pp. 123–138. Springer, Berlin (2010)

  31. Nance R.E.: A history of discrete event simulation programming languages. SIGPLAN Not. 28, 149–175 (1993)

    Article  Google Scholar 

  32. OCL. http://www.omg.org/spec/OCL/2.3/Beta2/

  33. Pegden, C.D.: SIMIO: a new simulation system based on intelligent objects. In: Winter Simulation Conference, pp. 2293–2300 (2007). http://www.simio.com

  34. Pegden, C.D., Davis, D.A.: Arena: a SIMAN/cinema-based hierarchical modeling system. In: Winter Simulation Conference, pp. 390–399 (1992). http://www.arenasimulation.com/

  35. Repenning, A., Ioannidou, A., Zola, J.: AgentSheets: end-user programmable simulations. J. Artif. Soc. Soc. Simul. 3(3) (2000). http://www.agentsheets.com

  36. Rivera, J.E., Durán, F., Vallecillo, A.: A graphical approach for modeling time-dependent behavior of DSLs. In: VL/HCC’09, pp. 51–55. IEEE (2009)

  37. Rivera, J.E., Durán, F., Vallecillo, A.: On the behavioral semantics of real-time domain specific visual languages. In: WRLA. LNCS, vol. 6381, pp. 174–190. Springer, Berlin (2010)

  38. Rivera, J.E., Guerra, E., de Lara, J., Vallecillo, A.: Analyzing rule-based behavioral semantics of visual modeling languages with Maude. In: SLE’09. LNCS, vol. 5452, pp. 54–73. Springer, Berlin (2009)

  39. Rozenberg, G. (ed.): Handbook of Graph Grammars and Computing by Graph Transformations. Foundations, vol. 1. World Scientific, Singapore (1997)

    Google Scholar 

  40. Schriber T.J.: Simulation Using GPSS. Wiley, New York (1974)

    MATH  Google Scholar 

  41. Schriber, T.J., Brunner, D.T.: Inside discrete-event simulation software: how it works and why it matters. In: Winter Simulation Conference, pp. 151–165 (2010)

  42. Schruben L.: Simulation modeling with event graphs. Commun. ACM 26(11), 957–963 (1983)

    Article  Google Scholar 

  43. Strobl, T., Minas, M.: Specifying and generating editing environments for interactive animated visual models. ECEASST, 29 (2010)

  44. Syriani, E., Vangheluwe, H.: Programmed graph rewriting with DEVS. In: AGTIVE’07. LNCS, vol. 5088, pp. 136–151. Springer, Berlin (2008)

  45. Torrini, P., Heckel, R., Ráth, I., Bergmann, G.: Stochastic graph transformation with regions. ECEASST, 29 (2010)

  46. Vaucher J.G., Duval P.: A comparison of simulation event list algorithms. Commun. ACM 18, 223–230 (1975)

    Article  MATH  Google Scholar 

  47. Zeigler B.P., Praehofer H., Kim T.G.: Theory of Modeling and Simulation, 2nd edn. Academic Press, New York (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Juan de Lara.

Additional information

Communicated by Dr. Andy Schürr and Arend Rensink.

Rights and permissions

Reprints and permissions

About this article

Cite this article

de Lara, J., Guerra, E., Boronat, A. et al. Domain-specific discrete event modelling and simulation using graph transformation. Softw Syst Model 13, 209–238 (2014). https://doi.org/10.1007/s10270-012-0242-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10270-012-0242-3

Keywords

Navigation