Runtime Prediction of Queued Behaviour

  • Nurzhan Duzbayev
  • Iman Poernomo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4214)


Service-based software architectures are often modeled with queues and queuing networks. Such models are useful for performance evaluation and design. They can also assist in runtime maintenance and administration, but, in this context, it is often far more valuable to be able to forecast how QoS characteristics are likely to evolve in the near future. This is particularly important in cases where systems can be adapted to counter QoS constraint violations: in such systems, given predictions of likely future QoS characteristics, pre-emptive adaptation strategies can be implemented.

This paper outlines an approach to runtime prediction of QoS characteristics of queued systems. Predictions are computed by applying ARIMA forecasting techniques to basic properties of a queued model, and then using the model to predict complex QoS characteristics. We outline how our methods integrate into our implementation framework for monitoring and pre-emptive adaptation of web service based systems.


Queue Length Policy Language Adaptation Policy Adaptation Engine Average Queue Length 
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.
    Al-Ali, R., Hafid, A., Rana, O., Walker, D.: An approach for quality of service adaptation in service-oriented grids. Concurrency and Computation: Practice and Experience 16(5), 401–412 (2004)CrossRefGoogle Scholar
  2. 2.
    Balsamo, S., Di Marco, A., Inverardi, P., Simeoni, M.: Model-based performance prediction in software development: A survey. IEEE Transactions On Software Engineering 30(5), 295–310 (2004)CrossRefGoogle Scholar
  3. 3.
    Chan, K., Poernomo, I.: Model driven instrumentation for monitoring quality of service. In: Tenth IEEE International EDOC Enterprise Computing (submitted, 2006)Google Scholar
  4. 4.
    Chan, K., Poernomo, I., Schmidt, H.W., Jayaputera, J.: A Model-Oriented Framework for Runtime Monitoring of Nonfunctional Properties. In: Reussner, R., Mayer, J., Stafford, J.A., Overhage, S., Becker, S., Schroeder, P.J. (eds.) QoSA 2005 and SOQUA 2005. LNCS, vol. 3712, pp. 38–52. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    Chatfield, C., Yar, M.: Holt-winters forecasting: some practical issues. The Statistician 37, 129–140 (1988)CrossRefGoogle Scholar
  6. 6.
    Cysneiros, L.M., do Prado, J.C.S.: Nonfunctional requirements: From elicitation to conceptual models. IEEE Transactions On Software Engineering 30(5), 328–350 (2004)CrossRefGoogle Scholar
  7. 7.
    Bunday, B.D.: An introduction to queueing theory. Halsted Press, New York (1996)zbMATHGoogle Scholar
  8. 8.
    Dinda, P.A.: Online prediction of the running time of tasks. In: Joint International Conference on Measurement and Modeling of Computer Systems, pp. 336–337 (May 2001)Google Scholar
  9. 9.
    DMTF. Common information model (CIM) specification, version 2.2 (June 14, 1999), See:
  10. 10.
    Gardner Jr., E.S.: Exponential smoothing: the state of the art. Forecasting 2, 1–28 (1985)CrossRefGoogle Scholar
  11. 11.
    Fortier, P.J., Michel, H.E.: Computer Systems Perfomance Evaluation and Prediction. Digital Press (2003)Google Scholar
  12. 12.
    Foss, S., Chernova, N.: On stability of a partially accessible multi-station queue with state-dependent routing. Queueing Systems 1(29), 55–73 (1998)CrossRefMathSciNetGoogle Scholar
  13. 13.
    Foss, S., Konstantopoulos, T.: An overview of some stochastic stability methods. Journal of the Operations Research Society of Japan 47(4), 275–303 (2003)MathSciNetGoogle Scholar
  14. 14.
    Object Management Group. Uml profile for modeling quality of service and fault tolerance characteristics and mechanisms (2005),
  15. 15.
    Heineman, G.T., Loyall, J.P., Schantz, R.E.: Component technology and qoS management. In: Crnković, I., Stafford, J.A., Schmidt, H.W., Wallnau, K. (eds.) CBSE 2004. LNCS, vol. 3054, pp. 249–263. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  16. 16.
    Januszewski, K.: Using UDDI at Run Time, Part II. Microsoft MSDN (accessed June 4, 2006),
  17. 17.
    Kleinrock, L.: Queueing Systems, vol. 1. J. Wiley, New York (1975)zbMATHGoogle Scholar
  18. 18.
    Sharma, P.K., Loyall, J.P., Heineman, G.T., Schantz, R.E., Shapiro, R., Duzan, G.: Component-based dynamic qos adaptations in distributed real-time and embedded systems. In: International Symposium on Distributed Objects and Applications (DOA), Agia Napa, Cyprus, pp. 1208–1224 (October 25-29, 2004)Google Scholar
  19. 19.
    Zeng, L., Benatallah, B., Ngu, A.H.H., Dumas, M., Kalagnanam, J., Chang, H.: Qos-aware middleware for web services composition. IEEE Transactions On Software Engineering 30(5), 311–327 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Nurzhan Duzbayev
    • 1
  • Iman Poernomo
    • 1
  1. 1.King’s College LondonStrand, LondonUK

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