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Towards Stochastic Performance Models for Web 2.0 Applications

  • Johannes Artner
  • Alexandra MazakEmail author
  • Manuel Wimmer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10360)

Abstract

System performance is one of the most critical quality characteristics of Web applications which is typically expressed in response time, throughput, and utilization. These performance indicators, as well as the workload of a system, may be evaluated and analyzed by (i) model-based or (ii) measurement-based techniques. Given the complementary benefits offered by both techniques, it seems beneficial to combine them. For this purpose we introduce a combined performance engineering approach by presenting a concise way of describing user behavior by Markov models and derive from them workloads on resources. By means of an empirical user test, we evaluate the Markov assumption for a given Web 2.0 application which is an important prerequisite for our approach.

Keywords

Web application performance engineering Markov models Queueing theory 

References

  1. 1.
    Barham, P., Donnelly, A., Isaacs, R., Mortier, R.: Using Magpie for request extraction and workload modelling. In: OSDI (2004)Google Scholar
  2. 2.
    Berardinelli, L., Maetzler, E., Mayerhofer, T., Wimmer, M.: Integrating performance modeling in industrial automation through AutomationML and PMIF. In: INDIN (2016)Google Scholar
  3. 3.
    Bolch, G., Greiner, S., de Meer, H., Trivedi, K.S.: Queueing Networks and Markov Chains: Modeling and Performance Evaluation with Computer Science Applications. Wiley, Hoboken (2006)CrossRefzbMATHGoogle Scholar
  4. 4.
    Borges, J., Levene, M.: Data mining of user navigation patterns. In: Masand, B., Spiliopoulou, M. (eds.) WebKDD 1999. LNCS, vol. 1836, pp. 92–112. Springer, Heidelberg (2000). doi: 10.1007/3-540-44934-5_6 CrossRefGoogle Scholar
  5. 5.
    Franks, G., Al-Omari, T., Woodside, M., Das, O., Derisavi, S.: Enhanced modeling and solution of layered queueing networks. IEEE TSE 35, 148–161 (2009)Google Scholar
  6. 6.
    Harchol-Balter, M.: Performance Modeling and Design of Computer Systems: Queueing Theory in Action, 1st edn. Cambridge University Press, New York (2013)zbMATHGoogle Scholar
  7. 7.
    Hevizi, G., Biczó, M., Poczos, B., Szabo, Z., Takics, B., Lorincz, A.: Hidden Markov model finds behavioral patterns of users working with a headmouse driven writing tool. In: IJCNN (2004)Google Scholar
  8. 8.
    Jain, R.: The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation, and Modeling. Wiley, New York (1991)zbMATHGoogle Scholar
  9. 9.
    Jespersen, S., Pedersen, T.B., Thorhauge, J.: Evaluating the markov assumption for web usage mining. In: WIDM (2003)Google Scholar
  10. 10.
    Kappel, G., Pröll, B., Reich, S., Retschitzegger, W.: Web Engineering. Wiley, New York (2006)Google Scholar
  11. 11.
    Kotsis, G., Pinzger, M.: AWPS – an architecture for pro-active web performance management. In: Hummel, K.A., Hlavacs, H., Gansterer, W. (eds.) PERFORM 2010. LNCS, vol. 6821, pp. 215–226. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-25575-5_18 CrossRefGoogle Scholar
  12. 12.
    Kraft, S., Pacheco-Sanchez, S., Casale, G., Dawson, S.: Estimating service resource consumption from response time measurements. In: VALUETOOLS (2009)Google Scholar
  13. 13.
    Li, Z., Tian, J.: Testing the suitability of Markov chains as Web usage models. In: COMPSAC (2003)Google Scholar
  14. 14.
    Petriu, D.B., Woodside, M.: An intermediate metamodel with scenarios and resources for generating performance models from uml designs. SoSyM 6(2), 163–184 (2007)Google Scholar
  15. 15.
    Rabiner, L.: A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77(2), 257–286 (1989)CrossRefGoogle Scholar
  16. 16.
    Rohr, M., Van Hoorn, A., Matevska, J., Sommer, N., Stoever, L., Giesecke, S., Hasselbring, W.: Kieker: continuous monitoring and on demand visualization of Java software behavior. In: IASTED-SE (2008)Google Scholar
  17. 17.
    Serfozo, R.: Basics of Applied Stochastic Processes. Springer, Heidelberg (2009)CrossRefzbMATHGoogle Scholar
  18. 18.
    Souza e Silva, E., Leão, R.M.M., Muntz, R.R.: Performance evaluation with hidden Markov models. In: Hummel, K.A., Hlavacs, H., Gansterer, W. (eds.) PERFORM 2010. LNCS, vol. 6821, pp. 112–128. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-25575-5_10 CrossRefGoogle Scholar
  19. 19.
    Smith, C.U., Lladó, C.M., Puigjaner, R.: Performance Model Interchange Format (PMIF 2): a comprehensive approach to queueing network model interoperability. Perform. Eval. 67(7), 548–568 (2010)CrossRefGoogle Scholar
  20. 20.
    Hoorn, A., Rohr, M., Hasselbring, W.: Generating probabilistic and intensity-varying workload for web-based software systems. In: Kounev, S., Gorton, I., Sachs, K. (eds.) SIPEW 2008. LNCS, vol. 5119, pp. 124–143. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-69814-2_9 CrossRefGoogle Scholar
  21. 21.
    Woodside, M., Petriu, D.C., Merseguer, J., Petriu, D.B., Alhaj, M.: Transformation challenges: from software models to performance models. SoSyM 13(4), 1529–1552 (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Johannes Artner
    • 1
  • Alexandra Mazak
    • 1
    Email author
  • Manuel Wimmer
    • 1
  1. 1.Business Informatics Group (BIG)TU WienViennaAustria

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