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Visualisation for Stochastic Process Algebras: The Graphic Truth

  • Michael J. A. Smith
  • Stephen Gilmore
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6977)

Abstract

There have historically been two approaches to performance modelling. On the one hand, textual language-based formalisms such as stochastic process algebras allow compositional modelling that is portable and easy to manage. In contrast, graphical formalisms such as stochastic Petri nets and stochastic activity networks provide an automaton-based view of the model, which may be easier to visualise, at the expense of portability. In this paper, we argue that we can achieve the benefits of both approaches by generating a graphical view of a stochastic process algebra model, which is synchronised with the textual representation, giving the user has two ways in which they can interact with the model.

We present a tool, as part of the PEPA Eclipse Plug-in, that allows the components of models in the Performance Evaluation Process Algebra (PEPA) to be visualised in a graphical way. This also provides a natural interface for labelling states in the model, which integrates with our interface for specifying and model checking properties in the Continuous Stochastic Logic (CSL). We describe recent improvements to the tool in terms of usability and exploiting the visualisation framework, and discuss some of the general features of the implementation that could be used by other tools. We illustrate the tool using an example based on a model of a financial web-service application.

Keywords

Model Check Process Algebra Atomic Property Sequential Component Service Idle 
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.
    Ai, N.: An Enhanced Abstraction View for the PEPA Eclipse Plug-in. Master’s thesis, School of Informatics, The University of Edinburgh (2010)Google Scholar
  2. 2.
    Aziz, A., Sanwal, K., Singhal, V., Brayton, R.: Verifying continuous time Markov chains. In: Alur, R., Henzinger, T.A. (eds.) CAV 1996. LNCS, vol. 1102, pp. 269–276. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  3. 3.
    Balbo, G.: Introduction to stochastic petri nets. In: Brinksma, E., Hermanns, H., Katoen, J.-P. (eds.) EEF School 2000 and FMPA 2000. LNCS, vol. 2090, pp. 84–155. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  4. 4.
    Bonet, P., Llado, C.M., Puijaner, R., Knottenbelt, W.J.: PIPE v2.5: A Petri Net Tool for Performance Modelling. In: 23rd Latin American Conference on Informatics (2007)Google Scholar
  5. 5.
    Bradley, J.T., Dingle, N.J., Gilmore, S.T., Knottenbelt, W.J.: Derivation of passage-time densities in PEPA models using IPC: The Imperial PEPA Compiler. In: Proceedings of the 11th IEEE/ACM International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunications Systems, pp. 344–351. IEEE Press, Los Alamitos (2003)Google Scholar
  6. 6.
    Calvarese, F., Di Marco, A., Malavolta, I.: Building graphical support for Aemilia ADL. Technical Report TRCS 008/2007, University of L’Aquila (2007)Google Scholar
  7. 7.
    Calzolai, F., De Nicola, R., Loreti, M., Tiezzi, F.: TAPAs: A tool for the analysis of process algebras. In: Jensen, K., van der Aalst, W.M.P., Billington, J. (eds.) Transactions on Petri Nets and Other Models of Concurrency I. LNCS, vol. 5100, pp. 54–70. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  8. 8.
    Canevet, C., Gilmore, S., Hillston, J., Prowse, M., Stevens, P.: Performance modelling with UML and stochastic process algebras. IEE Proceedings: Computers and Digital Techniques 150(2), 107–120 (2003)Google Scholar
  9. 9.
    Cappello, I., Clark, A., Gilmore, S., Latella, D., Loreti, M., Quaglia, P., Schivo, S.: Quantitative analysis of services. In: Rigorous Software Engineering for Service-Oriented Systems. Springer, Heidelberg (2011)Google Scholar
  10. 10.
    Clark, A., Gilmore, S.: State-aware performance analysis with eXtended stochastic probes. In: Thomas, N., Juiz, C. (eds.) EPEW 2008. LNCS, vol. 5261, pp. 125–140. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  11. 11.
    Daly, D., Deavours, D.D., Doyle, J.M., Stillman, A.J., Webster, P.G., Sanders, W.H.: Möbius: An extensible framework for performance and dependability modeling. In: Multi-Workshop on Formal Methods in Performance Evaluation and Applications (1999)Google Scholar
  12. 12.
    The Eclipse platform, http://www.eclipse.org
  13. 13.
    The Eclipse Graphical Modeling Framework (GMF), http://www.eclipse.org/modeling/gmf/
  14. 14.
    Gilmore, S., Gribaudo, M.: Graphical modelling of process algebras with DrawNET. In: Proceedings of the Tools Appendix to the International Multiconference on Measurement, Modelling and Evaluation of Computer-Communication Systems (2003)Google Scholar
  15. 15.
    Gilmore, S., Hillston, J.: The PEPA Workbench: A Tool to Support a Process Algebra-based Approach to Performance Modelling. In: Haring, G., Kotsis, G. (eds.) TOOLS 1994. LNCS, vol. 794, pp. 353–368. Springer, Heidelberg (1994)Google Scholar
  16. 16.
    Hillston, J.: A Compositional Approach to Performance Modelling. Cambridge University Press, Cambridge (1996)CrossRefzbMATHGoogle Scholar
  17. 17.
    Hillston, J., Kloul, L.: An efficient kronecker representation for PEPA models. In: de Luca, L., Gilmore, S. (eds.) PROBMIV 2001, PAPM-PROBMIV 2001, and PAPM 2001. LNCS, vol. 2165, pp. 120–135. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  18. 18.
    Katoen, J.-P., Khattri, M., Zapreevt, I.S.: A Markov reward model checker. In: Proceedings of the Second International Conference on the Quantitative Evaluation of Systems (QEST), pp. 243–244. IEEE Press, Los Alamitos (2005)CrossRefGoogle Scholar
  19. 19.
    Kwiatkowska, M., Norman, G., Parker, D.: PRISM 4.0: Verification of probabilistic real-time systems. In: Gopalakrishnan, G., Qadeer, S. (eds.) CAV 2011. LNCS, vol. 6806, pp. 585–591. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  20. 20.
    Movaghar, A., Meyer, J.F.: Performability modelling with stochastic activity networks. In: Proceedings of 1984 Real-Time Symposium, pp. 8–40 (1984)Google Scholar
  21. 21.
    Plateau, B.: On the stochastic structure of parallelism and synchronization models for distributed algorithms. SIGMETRICS Performance Evaluation Review 13(2), 147–154 (1985)CrossRefGoogle Scholar
  22. 22.
    Riedl, M., Schuster, J., Siegle, M.: Recent extensions to the stochastic process algebra tool CASPA. In: Proceedings of the 5th International Conference on the Quantitative Evaluation of Systems (QEST), pp. 113–114. IEEE Press, Los Alamitos (2008)Google Scholar
  23. 23.
    SENSORIA Web site. SENSORIA: Software engineering for service-oriented overlay computers (2011), http://www.sensoria-ist.edu
  24. 24.
    Smith, M.J.A.: Abstraction and model checking in the PEPA plug-in for Eclipse. In: Proceedings of the 7th International Conference on the Quantitative Evaluation of Systems (QEST), pp. 155–156. IEEE Press, Los Alamitos (2010)Google Scholar
  25. 25.
    Smith, M.J.A.: Compositional abstraction of PEPA models for transient analysis. In: Aldini, A., Bernardo, M., Bononi, L., Cortellessa, V. (eds.) EPEW 2010. LNCS, vol. 6342, pp. 252–267. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  26. 26.
    Smith, M.J.A.: Compositional abstractions for long-run properties of stochastic systems. In: Proceedings of the 8th International Conference on the Quantitative Evaluation of Systems (QEST). IEEE Press, Los Alamitos (2011)Google Scholar
  27. 27.
    Stefanek, A., Hayden, R.A., Bradley, J.T.: GPA — Tool for rapid analysis of very large scale PEPA models. In: Proceedings of the 26th UK Performance Engineering Workshop (UKPEW), pp. 91–101 (2010)Google Scholar
  28. 28.
    Suto, T., Bradley, J.T., Knottenbelt, W.J.: Performance trees: A new approach to quantitative performance specification. In: MASCOTS 2006, 14th International Symposium on Modelling, Analysis, and Simulation of Computer and Telecommunication Systems, pp. 303–313. IEEE Press, Los Alamitos (2006)Google Scholar
  29. 29.
    Thomas, N., Munro, M., King, P., Pooley, R.: Visual representation of stochastic process algebra models. In: Proceedings of the 2nd International Workshop on Software and Performance (WOSP), pp. 18–19. ACM, New York (2000)CrossRefGoogle Scholar
  30. 30.
    Tribastone, M., Duguid, A., Gilmore, S.: The PEPA Eclipse plugin. SIGMETRICS Performance Evaluation Review 36(4), 28–33 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Michael J. A. Smith
    • 1
  • Stephen Gilmore
    • 2
  1. 1.Department of Informatics and Mathematical ModellingDanmarks Tekniske UniversitetLyngbyDenmark
  2. 2.Laboratory for Foundations of Computer ScienceUniversity of EdinburghEdinburghUnited Kingdom

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