Predictive Modelling of Peer-to-Peer Event-Driven Communication in Component-Based Systems

  • Christoph Rathfelder
  • David Evans
  • Samuel Kounev
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6342)


The event-driven communication paradigm is used increasingly often to build loosely-coupled distributed systems in many industry domains including telecommunications, transportation, and supply chain management. However, the loose coupling of components in such systems makes it hard for developers to estimate their behaviour and performance under load. Most general purpose performance meta-models for component-based systems provide limited support for modelling event-driven communication. In this paper, we present a case study of a real-life road traffic monitoring system that shows how event-driven communication can be modelled for performance prediction and capacity planning. Our approach is based on the Palladio Component Model (PCM) which we have extended to support event-driven communication. We evaluate the accuracy of our modelling approach in a number of different workload and configuration scenarios. The results demonstrate the practicality and effectiveness of the proposed approach.


Model Transformation Business Logic Palladio Component Model Proximity Detector Layer Queueing Network 
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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  1. 1.
    Hohpe, G., Woolf, B.: Enterprise integration patterns: designing, building and deploying messaging solutions, 11th print edn. The Addison-Wesley signature series. Addison-Wesley, Boston (2008)Google Scholar
  2. 2.
    Koziolek, H.: Performance evaluation of component-based software systems: A survey. Performance Evaluation 67-8(8), 634–658 (2009); Special Issue on Software and PerformanceGoogle Scholar
  3. 3.
    Mühl, G., Schröter, A., Parzyjegla, H., Kounev, S., Richling, J.: Stochastic Analysis of Hierarchical Publish/Subscribe Systems. In: Sips, H., Epema, D., Lin, H.-X. (eds.) Euro-Par 2009 Parallel Processing. LNCS, vol. 5704, pp. 97–109. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  4. 4.
    Rathfelder, C., Kounev, S.: Position Paper: Modeling Event-Driven Service-Oriented Systems using the Palladio Component Model. In: Proc. of QUASOSS 2009, pp. 33–38. ACM, New York (2009)Google Scholar
  5. 5.
    Rathfelder, C., Kounev, S.: Fast Abstract: Model-based Performance Prediction for Event-driven Systems. In: DEBS 2009, Nashville, TN, USA (July 2009)Google Scholar
  6. 6.
    Becker, S., Koziolek, H., Reussner, R.: The Palladio component model for model-driven performance prediction. Journal of Systems and Software 82, 3–22 (2009)CrossRefGoogle Scholar
  7. 7.
    Bacon, J., Beresford, A.R., Evans, D., Ingram, D., Trigoni, N., Guitton, A., Skordylis, A.: TIME: An open platform for capturing, processing and delivering transport-related data. In: Proceedings of the IEEE Consumer Communications and Networking Conference, pp. 687–691 (2008)Google Scholar
  8. 8.
    Ingram, D.: Reconfigurable middleware for high availability sensor systems. In: Proc. of DEBS 2009. ACM Press, New York (2009)Google Scholar
  9. 9.
    Koziolek, H., Reussner, R.: A Model Transformation from the Palladio Component Model to Layered Queueing Networks. In: Kounev, S., Gorton, I., Sachs, K. (eds.) SIPEW 2008. LNCS, vol. 5119, pp. 58–78. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Happe, J.: Predicting Software Performance in Symmetric Multi-core and Multiprocessor Environments. Dissertation, University of Oldenburg, Germany (2008)Google Scholar
  11. 11.
    Becker, S.: Coupled Model Transformations for QoS Enabled Component-Based Software Design. Karlsruhe Series on Software Quality, vol. 1. Universitätsverlag Karlsruhe (2008)Google Scholar
  12. 12.
    Hunt, P.B., Robertson, D.I., Bretherton, R.D., Winton, R.I.: SCOOT—a traffic responsive method of coordinating signals. Technical Report LR1014, Transport and Road Research Laboratory (1981)Google Scholar
  13. 13.
    Smith, C.U.: Performance Engineering of Software Systems. Addison-Wesley Longman Publishing Co., Inc., Boston (1990)Google Scholar
  14. 14.
    Object Management Group (OMG): UML Profile for Schedulability, Performance, and Time (SPT), v1.1 (January 2005)Google Scholar
  15. 15.
    Object Management Group (OMG): UML Profile for Modeling and Analysis of Real-Time and Embedded systems (MARTE) (May 2006)Google Scholar
  16. 16.
    Marzolla, M.: Simulation-Based Performance Modeling of UML Software Architectures. PhD Thesis TD-2004-1, Dipartimento di Informatica, Università Ca’ Foscari di Venezia, Mestre, Italy (February 2004)Google Scholar
  17. 17.
    Petriu, D.C., Wang, X.: From UML description of high-level software architecture to LQN performance models. In: Münch, M., Nagl, M. (eds.) AGTIVE 1999. LNCS, vol. 1779, pp. 47–63. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  18. 18.
    Di Marco, A., Inveradi, P.: Compositional Generation of Software Architecture Performance QN Models. In: Proc. of WICSA 2004 (2004)Google Scholar
  19. 19.
    Cortellessa, V.: How far are we from the definition of a common software performance ontology? In: WOSP 2005: Proceedings of the 5th International Workshop on Software and Performance, pp. 195–204. ACM, New York (2005)Google Scholar
  20. 20.
    Cortellessa, V., Pierini, P., Rossi, D.: Integrating software models and platform models for performance analysis. IEEE Trans. on Softw. Eng. 33, 385–401 (2007)CrossRefGoogle Scholar
  21. 21.
    Cortellessa, V., Di Marco, A., Inverardi, P.: Integrating Performance and Reliability Analysis in a Non-Functional MDA Framework. In: Dwyer, M.B., Lopes, A. (eds.) FASE 2007. LNCS, vol. 4422, pp. 57–71. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  22. 22.
    Gu, G.P., Petriu, D.C.: XSLT transformation from UML models to LQN performance models. In: Proc. of WOSP 2002, pp. 227–234. ACM Press, New York (2002)Google Scholar
  23. 23.
    Gomaa, H., Menascé, D.A.: Design and performance modeling of component interconnection patterns for distributed software architectures. In: WOSP 2000: Proceedings of the 2nd International Workshop on Software and Performance, pp. 117–126. ACM, New York (2000)Google Scholar
  24. 24.
    Kounev, S., Sachs, K.: Benchmarking and Performance Modeling of Event-Based Systems. it - Information Technology 5 (October 2009), Survey PaperGoogle Scholar
  25. 25.
    Henjes, R., Menth, M., Zepfel, C.: Throughput Performance of Java Messaging Services Using WebsphereMQ. In: Proc. of ICDCSW 2006 (2006)Google Scholar
  26. 26.
    Menth, M., Henjes, R.: Analysis of the Message Waiting Time for the FioranoMQ JMS Server. In: Proc. of ICDCS 2006, Washington, DC, USA (2006)Google Scholar
  27. 27.
    Happe, J., Becker, S., Rathfelder, C., Friedrich, H., Reussner, R.H.: Parametric performance completions for model-driven performance prediction. Performance Evaluation 67(8), 694–716 (2010), Special Issue on Software and Performance CrossRefGoogle Scholar
  28. 28.
    Liu, Y., Gorton, I.: Performance Prediction of J2EE Applications Using Messaging Protocols. Component-Based Software Engineering, 1–16 (2005)Google Scholar
  29. 29.
    Kounev, S., Sachs, K., Bacon, J., Buchmann, A.: A methodology for performance modeling of distributed event-based systems. In: Proc. of the 11th IEEE Intl. Symposium on Object/Component/Service-oriented Real-time Distributed Computing (May 2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Christoph Rathfelder
    • 1
  • David Evans
    • 2
  • Samuel Kounev
    • 3
  1. 1.Software Engineering FZI Research Center for Information TechnologyKarlsruheGermany
  2. 2.Computer LaboratoryUniversity of Cambridge CambridgeUK
  3. 3.Faculty of InformaticsKarlsruhe Institute of Technology (KIT)KarlsruheGermany

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