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Simulation-Based Performance Analysis of Channel-Based Coordination Models

  • C. Verhoef
  • C. Krause
  • O. Kanters
  • R. van der Mei
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6721)

Abstract

Quantifying the performance of component-based or service-oriented systems is a complex task, e.g., it is non-trivial to calculate the end-to-end quality of service of a composite Web service. An established approach to reason about such systems in general is the use of coordination models, which can provide a formal basis for both their verification and implementation. An example of such a model is the channel-based coordination language Reo and its probabilistic extension Stochastic Reo. However, all existing performance analysis approaches for Stochastic Reo are restricted to the use of exponential distributions. To this end we introduce a transition structure, which enables a simulation approach for performance evaluation in Reo, enabling the use of arbitrary distributions and predefined probabilistic behaviors. Our approach supports steady-state and transient analysis and, moreover, scales much better than the existing automata-based algorithms.

Keywords

Boundary Node Discrete Event Simulator Average Queue Length Request Arrival Channel Delay 
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.
    Arbab, F.: Reo: A channel-based coordination model for component composition. Mathematical Structures in Computer Science 14, 329–366 (2004)CrossRefzbMATHMathSciNetGoogle Scholar
  2. 2.
    Arbab, F., Chothia, T., Mei, R., Meng, S., Moon, Y.J., Verhoef, C.: From coordination to stochastic models of QoS. In: Field, J., Vasconcelos, V.T. (eds.) COORDINATION 2009. LNCS, vol. 5521, pp. 268–287. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  3. 3.
    Kwiatkowska, M., Norman, G., Parker, D.: PRISM: Probabilistic symbolic model checker. In: Field, T., Harrison, P.G., Bradley, J., Harder, U. (eds.) TOOLS 2002. LNCS, vol. 2324, pp. 200–204. Springer, Heidelberg (2002)Google Scholar
  4. 4.
    Arbab, F., Chothia, T., Meng, S., Moon, Y.-J.: Component connectors with qoS guarantees. In: Murphy, A.L., Ryan, M. (eds.) COORDINATION 2007. LNCS, vol. 4467, pp. 286–304. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  5. 5.
    Chothia, T., Kleijn, J.: Q-Automata: Modelling the Resource Usage of Concurrent Components. ENTCS 175(2), 153–167 (2007)Google Scholar
  6. 6.
    Moon, Y.J., Silva, A., Krause, C., Arbab, F.: A compositional semantics for stochastic Reo connectors. In: Proc. of FOCLASA 2010, pp. 93–107 (2010)Google Scholar
  7. 7.
    Arbab, F., Meng, S., Moon, Y., Kwiatkowska, M., Qu, H.: Reo2MC: a tool chain for perf. anal. of coordination models. In: Proc. of FSE, pp. 287–288. ACM, New York (2009)Google Scholar
  8. 8.
    Clarke, D., Costa, D., Arbab, F.: Connector colouring I: Synchronisation and context dependency. Science of Computer Programming 66(3), 205–225 (2007)CrossRefzbMATHMathSciNetGoogle Scholar
  9. 9.
    Proença, J.: Deployment of Distributed Component Based Systems. PhD thesis, Leiden University, The Netherlands (2011)Google Scholar
  10. 10.
    ECT: Eclipse Coordination Tools (2011), http://reo.project.cwi.nl/
  11. 11.
    Gijsen, B., van der Mei, R., van den Berg, J.: An Integrated Performance Modeling Approach for Distributed Applications and ICT Systems. In: CMG-CONFERENCE. Computer Measurement Group; 1997, vol. 2, pp. 471–482 (2003)Google Scholar
  12. 12.
    Boxma, O., Daduna, H.: Sojourn times in queueing networks. Stochastic Analysis of Computer and Communication Systems, 401–450 (1990)Google Scholar
  13. 13.
    Rolia, J., Sevcik, K.: The method of layers. IEEE Transactions on Software Engineering 21(8), 689–700 (2002)CrossRefGoogle Scholar
  14. 14.
    Woodside, M., Neilson, J., Petriu, D., Majumdar, S.: The stochastic rendezvous network model for performance of synchronous client-server-like distributed software. IEEE Transactions on Computers 44(1), 20–34 (2002)CrossRefzbMATHGoogle Scholar
  15. 15.
    Smith, C.: Performance Engineering of Software Systems. Addison-Wesley, Reading (1990)Google Scholar
  16. 16.
    Torrini, P., Heckel, R., Ráth, I.: Stochastic simulation of graph transformation systems. In: Rosenblum, D.S., Taentzer, G. (eds.) FASE 2010. LNCS, vol. 6013, pp. 154–157. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  17. 17.
    Ledeczi, A., Davis, J., Neema, S., Agrawal, A.: Modeling methodology for integrated simulation of embedded systems. ACM Transactions on Modeling and Computer Simulation (TOMACS) 13(1), 82–103 (2003)CrossRefGoogle Scholar
  18. 18.
    Yacoub, S., Cukic, B., Ammar, H.: A scenario-based reliability anal. approach for component-based software. IEEE Trans. on Reliability 53(4), 465–480 (2004)CrossRefGoogle Scholar
  19. 19.
    Narayanan, S., McIlraith, S.: Simulation, verification and automated composition of web services. In: Proc. of the 11th Int. Conf. on WWW, pp. 77–88. ACM, New York (2002)Google Scholar
  20. 20.
    Ajmone Marsan, M., Conte, G., Balbo, G.: A class of generalized stochastic Petri nets for the performance evaluation of multiprocessor systems. ACM Transactions on Computer Systems (TOCS) 2(2), 93–122 (1984)CrossRefGoogle Scholar
  21. 21.
    Haverkort, B.R., Marie, R., Rubino, G., Trivedi, K.S. (eds.): Performability Modelling: Techniques and Tools. Wiley, Chichester (2001)zbMATHGoogle Scholar
  22. 22.
    Haas, P.: Stochastic petri nets: Modelling, stability, simulation. Springer, Heidelberg (2002)CrossRefzbMATHGoogle Scholar
  23. 23.
    Chiola, G., Franceschinis, G., Gaeta, R., Ribaudo, M.: GreatSPN 1.7: graphical editor and analyzer for timed and SPNs. Perf. Eval. 24(1-2), 47–68 (1995)CrossRefzbMATHGoogle Scholar
  24. 24.
    Fishman, G.: Principles of discrete event simulation. John Wiley, New York (1978)zbMATHGoogle Scholar
  25. 25.
    Glynn, P.W.: On the role of generalized semi-markov processes in simulation output analysis. In: Proc. WSC 1983, pp. 39–44. IEEE Press, Los Alamitos (1983)Google Scholar
  26. 26.
    Kanters, O., Verhoef, C., Schut, M.: QoS analysis by simulation in Reo. Vrije Universiteit Amsterdam, The Netherlands (2010)Google Scholar
  27. 27.
    Moon, Y., Arbab, F., Silva, A., Stam, A., Verhoef, C.: Stochastic Reo: A case Study (2011) (in preparation)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2011

Authors and Affiliations

  • C. Verhoef
    • 1
  • C. Krause
    • 2
  • O. Kanters
    • 1
  • R. van der Mei
    • 1
    • 3
  1. 1.Centrum Wiskunde & Informatica (CWI)AmsterdamThe Netherlands
  2. 2.Hasso Plattner Institute (HPI)University of PotsdamGermany
  3. 3.Vrije Universiteit Amsterdam (VUA)The Netherlands

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