Engineering Autonomic Controllers for Virtualized Web Applications

  • Giovanni Toffetti
  • Alessio Gambi
  • Mauro Pezzè
  • Cesare Pautasso
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6189)

Abstract

Modern Web applications are often hosted in a virtualized cloud computing infrastructure, and can dynamically scale in response to unpredictable changes in the workload to guarantee a given service level agreement. In this paper we propose to use Kriging surrogate models to approximate the performance profile of virtualized, multi-tier Web applications. The model is first built through a set of automated and controlled experiments at staging time, and can be later updated and refined by monitoring the Web application deployed in production. We claim that surrogate modeling makes a very good candidate for a model-driven approach to the engineering of an autonomic controller. Our experimental evaluation shows that the model predictions are faithful to the observed system’s performance, they improve with an increasing amount of samples and they can be computed quickly. We also provide evidence that the model can be effectively used to synthetize an aggregated objective function, a critical component of the autonomic controller. The approach is evaluated in the context of a RESTful Web service composition case study deployed on the RESERVOIR cloud.

References

  1. 1.
    Abrahao, B.D., Almeida, V., Almeida, J.M., Zhang, A., Beyer, D., Safai, F.: Self-adaptive SLA-driven capacity management for internet services. In: Proc. of IFIP/IEEE International Symposium on Integrated Network Management, pp. 557–568 (2006)Google Scholar
  2. 2.
    Almeida, V.A., Menascé, D.A.: Capacity planning: An essential tool for managing web services. IT Professional 4, 33–38 (2002)CrossRefGoogle Scholar
  3. 3.
    Cunha, I., Almeida, J.M., Almeida, V., Santos, M.: Self-adaptive capacity management for multi-tier virtualized environments. In: Proc. of IFIP/IEEE International Symposium on Integrated Network Management, pp. 129–138 (2007)Google Scholar
  4. 4.
    Brun, Y., Serugendo, G.D.M., Gacek, C., Giese, H., Kienle, H.M., Litoiu, M., Müller, H.A., Pezzè, M., Shaw, M.: Engineering self-adaptive systems through feedback loops. In: Cheng, B.H.C., de Lemos, R., Giese, H., Inverardi, P., Magee, J. (eds.) Software Engineering for Self-Adaptive Systems. LNCS, vol. 5525, pp. 48–70. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  5. 5.
    D’Ambrogio, A., Bocciarelli, P.: A model-driven approach to describe and predict the performance of composite services. In: Proc. of the 6th International Workshop on Software and Performance, pp. 78–89 (2007)Google Scholar
  6. 6.
    Duan, S., Babu, S.: Proactive identification of performance problems. In: Proc. of ACM SIGMOD international conference on Management of data, pp. 766–768 (2006)Google Scholar
  7. 7.
    Ghezzi, C., Tamburrelli, G.: Predicting performance properties for open systems with KAMI. In: Proc. of the International Conference on the Quality of Software Architectures, pp. 70–85 (2009)Google Scholar
  8. 8.
    IBM. An Architectural Blueprint for Autonomic Computing. Technical report, IBM (2003)Google Scholar
  9. 9.
    Jung, G., Joshi, K., Hiltunen, M., Schlichting, R., Pu, C.: Generating adaptation policies for multi-tier applications in consolidated server environments. In: Proc. of International Conference on Autonomic Computing, pp. 23–32 (2008)Google Scholar
  10. 10.
    Karlsson, M., Covell, M.: Dynamic black-box performance model estimation for self-tuning regulators. In: Proc. of the International Conference on Autonomic Computing, pp. 172–182 (2005)Google Scholar
  11. 11.
    Leitner, P., Wetzstein, B., Rosenberg, F., Michlmayr, A., Dustdar, S., Leymann, F.: Runtime prediction of service level agreement violations for composite services. In: Proc. of the Workshop on Non-Functional Properties and SLA Management in Service-Oriented Computing (2009)Google Scholar
  12. 12.
    Lenk, A., Klems, M., Nimis, J., Tai, S., Sandholm, T.: What’s inside the cloud? an architectural map of the cloud landscape. In: Proc. of the Workshop on Software Engineering Challenges of Cloud Computing, pp. 23–31 (2009)Google Scholar
  13. 13.
    Pautasso, C.: Composing RESTful services with JOpera. In: Bergel, A., Fabry, J. (eds.) Software Composition. LNCS, vol. 5634, pp. 142–159. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  14. 14.
    Pautasso, C., Alonso, G.: The jopera visual composition language. Journal of Visual Languages and Computing 16, 119–152 (2005)CrossRefGoogle Scholar
  15. 15.
    Rolia, J., Casale, G., Krishnamurthy, D., Dawson, S., Kraft, S.: Predictive modelling of SAP ERP applications: Challenges and solutions. In: Proc. of the International Workshop on Run-time mOdels for Self-managing Systems and Applications, pp. 2–10 (2009)Google Scholar
  16. 16.
    Sotomayor, B., Keahey, K., Foster, I.: Overhead matters: A model for virtual resource management. In: Proc. of International Workshop on Virtualization Technology in Distributed Computing, pp. 35–42 (2006)Google Scholar
  17. 17.
    Urgaonkar, B., Pacifici, G., Shenoy, P., Spreitzer, M., Tantawi, A.: Analytic modeling of multitier internet applications. ACM Transactions on the Web 1(1), 2–37 (2007)CrossRefGoogle Scholar
  18. 18.
    van Beers, W., Kleijnen, J.: Kriging interpolation in simulation: a survey. In: Proc. of Conference on Winter Simulation, pp. 113–121 (2004)Google Scholar
  19. 19.
    Wang, G.G., Shan, S.: Review of metamodeling techniques in support of engineering design optimization. Mechanical Design 129(4), 370–380 (2007)CrossRefMathSciNetGoogle Scholar
  20. 20.
    Wang, Y., Rutherford, M.J., Carzaniga, A., Wolf, A.L.: Automating experimentation on distributed testbeds. In: Proc. of International Conference on Automated Software Engineering, pp. 164–173 (2005)Google Scholar
  21. 21.
    Wei, Z., Dejun, J., Pierre, G., Chi, C.-H., van Steen, M.: Service-oriented data denormalization for scalable web applications. In: Proc. of the International Conference on World Wide Web, pp. 267–276 (2008)Google Scholar
  22. 22.
    Zhu, X., Uysal, M., Wang, Z., Singhal, S., Merchant, A., Padala, P., Shin, K.: What does control theory bring to systems research? SIGOPS Oper. Syst. Rev. 43(1), 62–69 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Giovanni Toffetti
    • 1
  • Alessio Gambi
    • 1
  • Mauro Pezzè
    • 1
    • 2
  • Cesare Pautasso
    • 1
  1. 1.University of LuganoLuganoSwitzerland
  2. 2.University of Milano BicoccaMilanItaly

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