Performance Aware Reconfiguration of Software Systems

  • Moreno Marzolla
  • Raffaela Mirandola
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6342)


In this paper we address the problem of building a scalable component-based system by means of dynamic reconfiguration. Specifically, we consider the system response time as the performance metric; we assume that the system components can be dynamically reconfigured to provide a degraded service with lower response time. Each component operating at one of the available quality levels is assigned a utility. Higher quality levels are associated to higher utility. We propose an approach for performance-aware reconfiguration of degradable software systems called PARSY (Performance Aware Reconfiguration of software SYstems). PARSY tunes individual components in order to maximize the system utility with the constraint of keeping the system response time below a pre defined threshold. PARSY uses a closed Queueing Network model to select the components to upgrade or degrade.


Response Time Quality Level Service Selection Service Demand Autonomic Computing 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chieu, T.C., Mohindra, A., Karve, A.A., Segal, A.: Dynamic scaling of web applications in a virtualized cloud computing environment. In: IEEE International Conference on E-Business Engineering, pp. 281–286. IEEE Computer Society, Los Alamitos (2009)Google Scholar
  2. 2.
    Martello, S., Toth, P.: Knapsack Problems: Algorithms and Computer Implementations. John Wiley and Sons, Chichester (1990)zbMATHGoogle Scholar
  3. 3.
    Zahorjan, J., Sevcick, K.C., Eager, D.L., Galler, B.I.: Balanced job bound analysis of queueing networks. Comm. ACM 25(2), 134–141 (1982)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Casolari, S., Colajanni, M., Lo Presti, F.: Runtime state change detector of computer system resources under non stationary conditions. In: Proc. 17th Int. Symp. on Modeling, Analysis and Simulation of Computer and Telecomunication Systems (MASCOTS 2009), London (September 2009)Google Scholar
  5. 5.
    Little, J.D.C.: A proof for the queuing formula: L = λW. Operations Research 9(3), 383–387 (1961)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Reiser, M., Lavenberg, S.S.: Mean-value analysis of closed multichain queuing networks. Journal of the ACM 27(2), 313–322 (1980)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Eaton, J.W.: GNU Octave Manual. Network Theory Limited (2002)Google Scholar
  8. 8.
    Cheng, B.H.C., de Lemos, R., Giese, H., Inverardi, P., Magee, J. (eds.): Software Engineering for Self-Adaptive Systems (outcome of a Dagstuhl Seminar). LNCS, vol. 5525. Springer, Heidelberg (2009)Google Scholar
  9. 9.
    Kephart, J.O., Chess, D.M.: The vision of autonomic computing. IEEE Computer 36(1), 41–50 (2003)CrossRefGoogle Scholar
  10. 10.
    Huebscher, M.C., McCann, J.A.: A survey of autonomic computing–degrees, models and applications. ACM Comput. Surv. 40(3) (2008)Google Scholar
  11. 11.
    Calinescu, R.: General-purpose autonomic computing. In: Denko, M.K., Yang, L.T., Zhang, Y. (eds.) Autonomic Computing and Networking, pp. 3–30. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  12. 12.
  13. 13.
    Calinescu, R., Kwiatkowska, M.: Using quantitative analysis to implement autonomic it systems. In: ICSE 2009: Proceedings of the 31st International Conference on Software Engineering, Washington, DC, USA, pp. 100–110. IEEE Computer Society, Los Alamitos (2009)CrossRefGoogle Scholar
  14. 14.
    Epifani, I., Ghezzi, C., Mirandola, R., Tamburrelli, G.: Model evolution by run-time parameter adaptation. In: Proc. 31st International Conference on Software Engineering (ICSE 2009), pp. 111–121. IEEE Computer Society, Los Alamitos (2009)Google Scholar
  15. 15.
    Raimondi, F., Skene, J., Emmerich, W.: Efficient online monitoring of web-service slas. In: SIGSOFT FSE, pp. 170–180. ACM, New York (2008)Google Scholar
  16. 16.
    Morin, B., Barais, O., Jézéquel, J.M., Fleurey, F., Solberg, A.: Models@ run.time to support dynamic adaptation. IEEE Computer 42(10), 44–51 (2009)CrossRefGoogle Scholar
  17. 17.
    Taylor, R.N., Medvidovic, N., Oreizy, P.: Architectural styles for runtime software adaptation. In: WICSA/ECSA, pp. 171–180. IEEE, Los Alamitos (2009)Google Scholar
  18. 18.
    Garlan, D., Cheng, S.W., Huang, A.C., Schmerl, B.R., Steenkiste, P.: Rainbow: Architecture-based self-adaptation with reusable infrastructure. IEEE Computer 37(10), 46–54 (2004)CrossRefGoogle Scholar
  19. 19.
    Maoz, S.: Using model-based traces as runtime models. IEEE Computer 42(10), 28–36 (2009)CrossRefGoogle Scholar
  20. 20.
    Zheng, T., Woodside, C.M., Litoiu, M.: Performance model estimation and tracking using optimal filters. IEEE Trans. Soft. Eng. 34(3), 391–406 (2008)CrossRefGoogle Scholar
  21. 21.
    Ardagna, D., Pernici, B.: Adaptive service composition in flexible processes. IEEE Trans. Soft. Eng. 33(6), 369–384 (2007)CrossRefGoogle Scholar
  22. 22.
    Canfora, G., Penta, M.D., Esposito, R., Villani, M.L.: A framework for QoS-aware binding and re-binding of composite web services. Journal of Systems and Software 81(10), 1754–1769 (2008)CrossRefGoogle Scholar
  23. 23.
    Cardellini, V., Casalicchio, E., Grassi, V., Lo Presti, F.: Scalable service selection for web service composition supporting differentiated QoS classes. Technical Report RR-07.59, Dip. di Informatica, Sistemi e Produzione, Università di Roma Tor Vergata (2007)Google Scholar
  24. 24.
    Zeng, L., Benatallah, B., Ngu, A.H.H., Dumas, M., Kalagnanam, J., Chang, H.: QoS-aware middleware for web services composition. IEEE Trans. Soft. Eng. 30(5), 311–327 (2004)CrossRefGoogle Scholar
  25. 25.
    Chafle, G., Doshi, P., Harney, J., Mittal, S., Srivastava, B.: Improved adaptation of web service compositions using value of changed information. In: ICWS, pp. 784–791. IEEE Computer Society, Los Alamitos (2007)Google Scholar
  26. 26.
    Guo, H., Huai, J., Li, H., Deng, T., Li, Y., Du, Z.: Angel: Optimal configuration for high available service composition. In: 2007 IEEE International Conference on Web Services (ICWS 2007), pp. 280–287. IEEE Computer Society, Los Alamitos (2007)CrossRefGoogle Scholar
  27. 27.
    Harney, J., Doshi, P.: Speeding up adaptation of web service compositions using expiration times. In: WWW 2007: Proceedings of the 16th International Conference on World Wide Web, pp. 1023–1032. ACM, New York (2007)Google Scholar
  28. 28.
    Cardellini, V., Casalicchio, E., Grassi, V., Lo Presti, F., Mirandola, R.: Qos-driven runtime adaptation of service oriented architectures. In: ESEC/FSE 2009: Proc. 7th Joint Meeting of the European Softw. Eng. Conf. and the ACM SIGSOFT Symp. on The Foundations of Softw. Eng., pp. 131–140. ACM, New York (2009)Google Scholar
  29. 29.
    Salehie, M., Li, S., Asadollahi, R., Tahvildari, L.: Change support in adaptive software: A case study for fine-grained adaptation. In: EASE 2009: Proc. Sixth IEEE Conf. and Workshops on Engineering of Autonomic and Autonomous Systems, Washington, DC, USA, pp. 35–44. IEEE Computer Society, Los Alamitos (2009)CrossRefGoogle Scholar
  30. 30.
    Martens, A., Koziolek, H., Becker, S., Reussner, R.: Automatically improve software architecture models for performance, reliability, and cost using evolutionary algorithms. In: Proc. First Joint WOSP/SIPEW International Conference on Performance Engineering, pp. 105–116. ACM, New York (2010)CrossRefGoogle Scholar
  31. 31.
    Menascé, D.A., Ewing, J.M., Gomaa, H., Malex, S., Sousa, J.P.: A framework for utility-based service oriented design in sassy. In: Proc. First Joint WOSP/SIPEW Int. Conf. on Performance Engineering, pp. 27–36. ACM, New York (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Moreno Marzolla
    • 1
  • Raffaela Mirandola
    • 2
  1. 1.Dipartimento di Scienze dell’InformazioneUniversità di BolognaBolognaItaly
  2. 2.Dipartimento di Elettronica e Informazione Piazza Leonardo da VinciPolitecnico di MilanoMilanoItaly

Personalised recommendations