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End-to-End Performance of Web Services

  • Paolo Cremonesi
  • Giuseppe Serazzi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2459)

Abstract

As the number of applications that are made available over the Internet rapidly grows, providing services with adequate performance becomes an increasingly critical issue. The performance requirements of the newapplications span from fewmilliseconds to hundreds of seconds. In spite of the continuous technological improvement (e.g., faster servers and clients, multi-threaded browsers supporting several simultaneous and persistent TCP connections, access to the network with larger bandwidth for both servers and clients), the network performance as captured by response time and throughput does not keep up and progressively degrades. Several are the causes of the poor “Quality of Web Services” that users very often experience. The characteristics of the traffic (self-similarity and heavy-tailedness) and the widely varying resource requirements (in terms of bandwidth, size and number of downloaded objects, processor time, number of I/Os, etc.) of web requests are among the most important ones. Other factors refer to the architectural complexity of the network path connecting the client browser to the web server and to the protocols behavior at the different layers.

In this paper we present a study of the performance of web services. The first part of the paper is devoted to the analysis of the origins of the fluctuations in web data traffic. This peculiar characteristic is one of the most important causes of the performance degradation of web applications. In the second part of the paper experimental measurements of performance indices, such as end-to-end response time, TCP connection time, transfer time, of several web applications are presented. The presence of self-similarity characteristics in the traffic measurements is shown.

Keywords

Hurst Parameter Download Time Congestion Control Mechanism Connection Time 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.

References

  1. 1.
    Abry, P. and Veitch, D.: Wavelet analysis of long-range dependent traffic. IEEE Trans. on Information Theory 44 (1998) 2–15.zbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Barford, P., Bestavros, A., Bradley, A., Crovella, M.E.: Changes in Web Client Access Patterns: Characteristics and Caching Implications. World Wide Web Journal 2 (1999) 15–28.CrossRefGoogle Scholar
  3. 3.
    Bhatti, N., Bouch, A., Kuchinsky, A.: Integrating User-Perceived Quality intoWeb Server Design. Proc. of the 9th InternationalWorld-WideWeb Conference. Elsevier (2000) 1–16.Google Scholar
  4. 4.
    Crovella, M.E., Bestavros, A.: Self-Similarity in World Wide Web traffic evidence and possible causes. IEEE/ACM Trans. on Networking 5 (1997) 835–846.CrossRefGoogle Scholar
  5. 5.
    Feldmann, A., Whitt. W.: Fitting mixtures of exponentials to long-tail distributions to analyze network performance models. Performance Evaluation 31 (1998) 245–279.CrossRefGoogle Scholar
  6. 6.
    Haverkort, B.R.: Performance of Computer Communication System: A Modelbased Approach. Wiley, New York (1998).Google Scholar
  7. 7.
  8. 8.
    Jackson, J.R.: Network of waiting lines. Oper. Res. 5 (1957) 518–521.CrossRefGoogle Scholar
  9. 9.
    Leland, W.E., Taqqu, M.S., Willinger, W., Wilson, D.V.: On the Self-Similar Nature of Ethernet Traffic. IEEE/ACM Trans. on Networking 2 (1994), 1–15.CrossRefGoogle Scholar
  10. 10.
    Nielsen, J.: Designing Web Usability. New Riders (2000).Google Scholar
  11. 11.
    Park, K., Kim, G., Crovella, M.E.: On the Effect of Traffic Self-similarity on Network Performance. Proc. of the SPIE International Conference on Performance and Control of Network Systems (1997) 296–310.Google Scholar
  12. 12.
    Paxon, V., Floyd, S.: Wide area traffic: The failure of Poisson modeling. IEEE/ACM Trans. on Networking 3 (1995) 226–244.CrossRefGoogle Scholar
  13. 13.
    Ramsay, J., Barbesi, A., Preece, J.: A psychological Investigation of Long Retrieval Times on the World Wide Web. Interacting with Computers 10 (1998) 77–86.CrossRefGoogle Scholar
  14. 14.
    Selvidge, P.R., Chaparro, B., Bender, G.T.: The World Wide Wait: Effects of Delays on User Performance. Proc. of the IEA 2000/HFES 2000 Congress (2000) 416–419.Google Scholar
  15. 15.
    Trivedi, K.S.: Probability and Statistics with Reliability, Queueing and Computer Science Applications. Wiley, New York (2002).Google Scholar
  16. 16.
    Willinger, W., Paxon, V., Taqqu, M.S.: Self-Similarity and Heavy-Tails: Structural Modeling of Network Traffic. In A Practical Guide To Heavy Tails: Statistical Techniques and Applications. R. Adler, R. Feldman and M. Taqqu Eds., Birkhauser, Boston (1998) 27–53.Google Scholar
  17. 17.
    Willinger, W., Taqqu, M.S., Sherman, R., Wilson, D.V.: Self-Similarity Through High-Variability: Statistical Analysis of Ethernet LAN Traffic at the Source Level. IEEE/ACM Trans. on Networking 5 (1997) 71–86.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Paolo Cremonesi
    • 1
  • Giuseppe Serazzi
    • 1
  1. 1.Dipartimento di Elettronica e InformazionePolitecnico di MilanoItaly

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