Model Identification for Energy-Aware Management of Web Service Systems

  • Mara Tanelli
  • Danilo Ardagna
  • Marco Lovera
  • Li Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5364)


In SOA environments, service providers need to comply with the service level objectives stipulated in contracts with their customers while minimizing the operating costs of the physical infrastructure, mainly related to energy costs. The problem can be effectively formalized by using system identification and control theory: the service levels are translated into set-points for the response times of the hosted applications, and performance are traded-off with energy saving objectives based on suitable models for server dynamics. As the behavior of the incoming workload changes significantly within a single business day, control-oriented system identification approaches are very promising to model such systems, especially at a very fine grained time scales and in transient conditions. In this paper Linear Parameter Varying (LPV) state space system identification algorithms are analyzed for modeling Web services systems. The suitability of LPV models is investigated and their performance assessed by experimental data.


Request Rate Dynamic Voltage Scaling Linear Parametrically Vary Generalize Processor Sharing Server Response Time 
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.


  1. 1.
    Abdelzaher, T., Shin, K.G., Bhatti, N.: Performance Guarantees for Web Server End-Systems: A Control-Theoretical Approach. IEEE Trans. on Parallel and Distributed Systems 15(2) (March 2002)Google Scholar
  2. 2.
    Andreolini, M., Casolari, S.: Load prediction models in web-based systems. In: Proc. of the 1st international conference on Perf. evaluation methodologies and tools, Pisa, Italy (2006)Google Scholar
  3. 3.
    Apkarian, P., Adams, R.J.: Advanced Gain-Scheduling Techniques for Uncertain Systems. IEEE Trans. on Control System Technology 6, 21–32 (1998)CrossRefzbMATHGoogle Scholar
  4. 4.
    Ardagna, D., Trubian, M., Zhang, L.: SLA based resource allocation policies in autonomic environments. Journal of Parallel and Distributed Computing 67(3), 259–270 (2007)CrossRefzbMATHGoogle Scholar
  5. 5.
    Casale, G., Mi, N., Smirni, E.: Bound Analysis of Closed Queueing Networks with Workload Burstiness. In: Proc. of SIGMETRICS (2008)Google Scholar
  6. 6.
    Chase, J.S., Anderson, D.C.: Managing Energy and Server Resources in Hosting Centers. In: ACM Symposium on Operating Systems principles (2001)Google Scholar
  7. 7.
  8. 8.
  9. 9.
    Kleinrock, L.: Queueing Systems. John Wiley and Sons, Chichester (1975)zbMATHGoogle Scholar
  10. 10.
    Kusic, D., Kandasamy, N.: Risk-Aware Limited Lookahead Control for Dynamic Resource Provisioning in Enterprise Computing Systems. In: ICSOC 2006 Proc. (2006)Google Scholar
  11. 11.
    Lee, L.H., Poolla, K.: Identification of linear parameter-varying systems using nonlinear programming. ASME J. of Dynamic Systems, Measurement and Control 121(1), 71–78 (1999)CrossRefGoogle Scholar
  12. 12.
    Metha, V.: A Holistic Solution to the IT Energy Crisis (2007),
  13. 13.
    Qin, W., Wang, Q.: Modeling and control design for performance management of web servers via an LPV approach. IEEE Trans. on Control Systems Tech. 15(2), 259–275 (2007)CrossRefGoogle Scholar
  14. 14.
    Riska, A., Squillante, M., Yu, S.Z., Liu, Z., Zhang, L.: Matrix-Analytic Analysis of a MAP/PH/1 Queue Fitted to Web Server Data. In: Latouche, G., Taylor, P. (eds.) Matrix-Analytic Methods: Theory and Applications, pp. 335–356. World Scientific, Singapore (2002)Google Scholar
  15. 15.
    Robertsson, A., Wittenmark, B., Kihl, M., Andersson, M.: Admission control for web server systems - design and experimental evaluation. In: 43rd IEEE Conference on Decision and Control (2004)Google Scholar
  16. 16.
    Tanelli, M., Ardagna, D., Lovera, M.: LPV model identification for power management of web service systems. In: 2008 IEEE Multi-conference on Systems and Control, San Antonio, USA (2008)Google Scholar
  17. 17.
    Toth, R., Felici, F., Heuberger, P.S.C., Van den Hof, P.M.J.: Discrete time LPV I/O and state space representations, differences of behavior and pitfalls of interpolation. In: Proc. of the 2007 European Control Conference, Kos, Greece (2007)Google Scholar
  18. 18.
    Urgaonkar, B., Pacifici, G., Shenoy, P.J., Spreitzer, M., Tantawi, A.N.: Analytic modeling of multitier Internet applications. ACM Transaction on Web 1(1) (January 2007)Google Scholar
  19. 19.
    Verdult, V.: Nonlinear System Identification: A State-Space Approach. PhD thesis, University of Twente, Faculty of Applied Physics, Enschede, The Netherlands (2002)Google Scholar
  20. 20.
    Verhaegen, M.: Identification of the deterministic part of MIMO state space models given in innovations form from input output data. Automatica 30, 61–74 (1994)MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Mara Tanelli
    • 1
    • 2
  • Danilo Ardagna
    • 1
  • Marco Lovera
    • 1
  • Li Zhang
    • 3
  1. 1.Dipartimento di Elettronica e InformazionePolitecnico di MilanoMilanoItaly
  2. 2.Dipartimento di Ingegneria dell’Informazione e Metodi MatematiciUniversità degli studi di BergamoDalmine (BG)Italy
  3. 3.IBM Research, T.J. Watson Research CenterYorktown Heights

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