International Conference on Formal Modeling and Analysis of Timed Systems

FORMATS 2015: Formal Modeling and Analysis of Timed Systems pp 76-92 | Cite as

Quantitative Analysis of Communication Scenarios

Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9268)

Abstract

Message sequence charts (MSCs) and their higher-order formalism in terms of message sequence graphs (MSGs) provide an intuitive way to describe communication scenarios. Naturally, quantitative aspects such as the probability of failure, maximal latency or the expected energy consumption play a crucial role for communication systems. In this paper, we introduce quantitative MSGs with costs or rewards and stochastic timing information in terms of rates. To perform a quantitative analysis, we propose a branching-time semantics for MSGs as possibly infinite continuous-time Markov chains (CTMCs) interpreting delayed choice on the partial-order semantics of MSGs. Whereas for locally synchronized MSGs a finite-state bisimulation quotient can be found and thus, standard algorithms for CTMCs can be applied, this is not the case in general. However, using a truncation-based approach we show how approximative solutions can be obtained. Our implementation shows feasibility of this approach exploiting reliability, resilience and energy consumption.

Keywords

Model Check Transition System Reachability Problem Communication Scenario Message Sequence Chart 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References

  1. 1.
    Akshay, S., Gastin, P., Mukund, M., Kumar, K.N.: Model checking time-constrained scenario-based specifications. In: FSTTCS 2010, pp. 204–215 (2010)Google Scholar
  2. 2.
    Akshay, S., Genest, B., Hélouët, L., Yang, S.: Regular set of representatives for time-constrained MSC graphs. Inf. Process. Lett. 112(14–15), 592–598 (2012)CrossRefMATHGoogle Scholar
  3. 3.
    Alur, R., Etessami, K., Yannakakis, M.: Inference of message sequence charts. IEEE Transactions on Software Engineering 29(7), 623–633 (2003)CrossRefGoogle Scholar
  4. 4.
    Alur, R., Holzmann, G.J., Peled, D.: An analyzer for message sequence charts. In: Software Concepts and Tools, pp. 304–313 (1996)Google Scholar
  5. 5.
    Alur, R., Yannakakis, M.: Model checking of message sequence charts. In: Baeten, J.C.M., Mauw, S. (eds.) CONCUR 1999. LNCS, vol. 1664, pp. 114–129. Springer, Heidelberg (1999) CrossRefGoogle Scholar
  6. 6.
    Baier, C., Cloth, L., Haverkort, B., Kuntz, M., Siegle, M.: Model checking Markov chains with actions and state labels. IEEE Transactions on Software Engineering 33(4), 209–224 (2007)CrossRefGoogle Scholar
  7. 7.
    Baier, C., Haverkort, B., Hermanns, H., Katoen, J.: Model-checking algorithms for continuous-time Markov chains. IEEE Transactions on Software Engineering 29(6), 524–541 (2003)CrossRefGoogle Scholar
  8. 8.
    Baier, C., Katoen, J.-P.: Principles of model checking. The MIT Press (2008)Google Scholar
  9. 9.
    Bollig, B., Leucker, M., Lucas, P.: Analysing message sequence graph specifications. In: Baaz, M., Voronkov, A. (eds.) LPAR 2002. LNCS (LNAI), vol. 2514, pp. 68–85. Springer, Heidelberg (2002) CrossRefGoogle Scholar
  10. 10.
    Chakraborty, J., D’Souza, D., Narayan Kumar, K.: Analysing message sequence graph specifications. In: Margaria, T., Steffen, B. (eds.) ISoLA 2010, Part I. LNCS, vol. 6415, pp. 549–563. Springer, Heidelberg (2010) CrossRefGoogle Scholar
  11. 11.
    Daly, D., Deavours, D.D., Doyle, J.M., Webster, P.G., Sanders, W.H.: Möbius: an extensible tool for performance and dependability modeling. In: Haverkort, B.R., Bohnenkamp, H.C., Smith, C.U. (eds.) TOOLS 2000. LNCS, vol. 1786, pp. 332–336. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  12. 12.
    Fantechi, A., Gnesi, S., Ristori, G.: Model checking for action-based logics. Formal Methods in System Design 4(2), 187–203 (1994)CrossRefMATHGoogle Scholar
  13. 13.
    Gazagnaire, T., Genest, B., Hélouët, L., Thiagarajan, P.S., Yang, S.: Causal message sequence charts. Theor. Comput. Sci. 410(41), 4094–4110 (2009)CrossRefMATHGoogle Scholar
  14. 14.
    Genest, B., Kuske, D., Muscholl, A.: A Kleene theorem and model-checking algorithms for existentially bounded communicating automata. Inf. Comput. 204(6)Google Scholar
  15. 15.
    Gunter, E.L., Muscholl, A., Peled, D.A.: Compositional message sequence charts. In: Margaria, T., Yi, W. (eds.) TACAS 2001. LNCS, vol. 2031, pp. 496–511. Springer, Heidelberg (2001) CrossRefGoogle Scholar
  16. 16.
    Hahn, E.M., Hermanns, H., Wachter, B., Zhang, L.: Time-bounded model checking of infinite-state continuous-time markov chains. Fundamenta Informaticae 95(1), 129–155 (2009)MathSciNetMATHGoogle Scholar
  17. 17.
    Hélouët, L., Jard, C., Caillaud, B.: An event structure based semantics for high-level message sequence charts. Math. Struct. in CS 12(4), 377–402 (2002)MATHGoogle Scholar
  18. 18.
    Henzinger, T.A., Mateescu, M., Wolf, V.: Sliding window abstraction for infinite markov chains. In: Bouajjani, A., Maler, O. (eds.) CAV 2009. LNCS, vol. 5643, pp. 337–352. Springer, Heidelberg (2009) CrossRefGoogle Scholar
  19. 19.
    Hillston, J.: A Compositional Approach to Performance Modelling. Cambridge University Press (1996)Google Scholar
  20. 20.
    Hinton, A., Kwiatkowska, M., Norman, G., Parker, D.: PRISM: A tool for automatic verification of probabilistic systems. In: Hermanns, H., Palsberg, J. (eds.) TACAS 2006. LNCS, vol. 3920, pp. 441–444. Springer, Heidelberg (2006) CrossRefGoogle Scholar
  21. 21.
    ITU-T. Annex b: Formal semantics of message sequence charts. Z.120, v2.2 (1998)Google Scholar
  22. 22.
    ITU-T. Message Sequence Chart (MSC). Z.120, Edition 5.0 (2011)Google Scholar
  23. 23.
    Kumar, K.N.: The theory of message sequence charts. In: Modern Applications of Automata Theory, pp. 289–324 (2012)Google Scholar
  24. 24.
    Lucas, P.: Timed semantics of message sequence charts based on timed automata. Electronic Notes in Theoretical Computer Science 65(6), 160–179 (2002)CrossRefGoogle Scholar
  25. 25.
    Madhusudan, P.: Reasoning about sequential and branching behaviours of message sequence graphs. In: Orejas, F., Spirakis, P.G., van Leeuwen, J. (eds.) ICALP 2001. LNCS, vol. 2076, pp. 809–820. Springer, Heidelberg (2001) CrossRefGoogle Scholar
  26. 26.
    Madhusudan, P., Meenakshi, B.: Beyond message sequence graphs. In: Hariharan, R., Mukund, M., Vinay, V. (eds.) FSTTCS 2001. LNCS, vol. 2245, pp. 256–267. Springer, Heidelberg (2001) CrossRefGoogle Scholar
  27. 27.
    Mauw, S., Reniers, M.A.: High-level message sequence charts. In: SDL, Forum, pp. 291–306 (1997)Google Scholar
  28. 28.
    Mousa, A.M.: Prospective of fifth generation mobile communications. International Journal of Next-Generation Networks (2012)Google Scholar
  29. 29.
    Muscholl, A., Peled, D.: Message sequence graphs and decision problems on mazurkiewicz traces. In: Kutyłowski, M., Pacholski, L., Wierzbicki, T. (eds.) MFCS 1999. LNCS, vol. 1672, pp. 81–91. Springer, Heidelberg (1999) CrossRefGoogle Scholar
  30. 30.
    Muscholl, A., Peled, D.A.: Deciding properties of message sequence charts. In: Leue, S., Systä, T.J. (eds.) Scenarios: Models, Transformations and Tools. LNCS, vol. 3466, pp. 43–65. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  31. 31.
    O’Brien, K., Salyers, D., Striegel, A., Poellabauer, C.: Power and performance characteristics of usb flash drives. In: WoWMoM 2008, pp. 1–4, June 2008Google Scholar
  32. 32.
    Pooley, R., King, P.: The unified modeling language and performance engineering. IEEE Proceedings - Software 149, 2–10 (1999)CrossRefGoogle Scholar
  33. 33.
    Remke, A., Haverkort, B.R.: CSL model checking algorithms for infinite-state structured markov chains. In: Raskin, J.-F., Thiagarajan, P.S. (eds.) FORMATS 2007. LNCS, vol. 4763, pp. 336–351. Springer, Heidelberg (2007) CrossRefGoogle Scholar
  34. 34.
    Reniers, M.A.: Message Sequence Chart: Syntax and Semantics. PhD thesis, Eindhoven University of Technology, June 1999Google Scholar
  35. 35.
    Hammouda, I., Hautamäki, J., Pussinen, M., Koskimies, K.: Managing variability using heterogeneous feature variation patterns. In: Cerioli, M. (ed.) FASE 2005. LNCS, vol. 3442, pp. 145–159. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  36. 36.
    Tanenbaum, A.S., Wetherall, D.J.: Computer Networks, 5th edn. Prentice Hall (2011)Google Scholar
  37. 37.
    Zhou, Z., Sheldon, F.T., Potok, T. E.: Modeling with stochastic message sequence charts. In: IIIS Proceedings of the International Conference on Computer, Communication, and Control Technology (CCCT 2003) (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Faculty of Computer ScienceTechnische Universität DresdenDresdenGermany

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