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Short-Term Spatio-temporal Forecasts of Web Performance by Means of Turning Bands Method

  • Leszek Borzemski
  • Michal Danielak
  • Anna Kaminska-Chuchmala
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7654)

Abstract

This work presents Turning Bands simulation method (TB) as a geostatistical approach for making spatio-temporal forecasts of Web performance. The most significant advantage of this method is requirement for the minimum amount of input data to make accurate and detailed forecasts. For this paper, necessary data were obtained with the Multiagent Internet Measuring System (MWING); however, only those measurements of European servers that were collected by the MWING’s agent in Gdansk were used. The aforementioned agent performed measurements (i.e. download times of the same given resource from the evaluated servers) three times every day, between 07.02.2009 and 28.02.2009, at 06:00 am, 12:00 pm and 06.00 pm. First, the preliminary and structural analyses of the measurement data were performed. Then short-term spatio-temporal forecasts of total downloading times for a four days ahead were made. And finally, thorough analysis of the obtained results was carried out and further research directions were proposed.

Keywords

Web performance spatio-temporal forecasts geostatistics Turning Bands method 

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References

  1. 1.
    Cisco Visual Networking Index (VNI) Global Mobile Data Traffic Forecast for 2011 to 2016, http://www.cisco.com/en/US/solutions/collateral/ns341/ns525/ns537/ns705/ns827/white_paper_c11-520862.pdf
  2. 2.
    CAIDA (Cooperative Association for Internet Data Analysis), http://caida.org
  3. 3.
    Mirza, M., Sommers, J., Barford, P., Zhu, X.: A machine learning approach to TCP throughput prediction. IEEE ACM T. Network 18(4), 1026–1039 (2010)CrossRefGoogle Scholar
  4. 4.
    Karrer, R.: TCP prediction for adaptive applications. In: Proc. 32nd IEEE Conference on Local Computer Networks, pp. 989–996 (2007)Google Scholar
  5. 5.
    He, Q., Dovrolis, C., Ammar, M.: On the predictability of large transfer TCP throughput. Comput. Netw. 51(14), 3959–3977 (2007)zbMATHCrossRefGoogle Scholar
  6. 6.
    Yin, D., Yildirim, E., Kulasekaran, S., Ross, B., Kosar, T.: A data throughput prediction and optimization service for widely distributed many-task computing. IEEE Trans. Parall. Distr. 22(6), 899–909 (2011)CrossRefGoogle Scholar
  7. 7.
    Borzemski, L.: Internet path behavior prediction via data mining: Conceptual framework and case study. J. Univers. Comput. Sci. 13(2), 287–316 (2007)Google Scholar
  8. 8.
    Sunila, R., Kollo, K.: A comparison of geostatistics and fuzzy application for digital elevation model. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XXXVI-2/C43 (2007)Google Scholar
  9. 9.
    Amiri, A., Gerdtham, U.: Relationship between exports, imports, and economic growth in France: evidence from cointegration analysis and Granger causality with using geostatistical models. Munich Personal RePEc Archive Paper No. 34190 (2011)Google Scholar
  10. 10.
    Wang, Y., Zhuang, D., Liu, H.: Spatial Distribution of Floating Car Speed. Journal of Transportation Systems Engineering and Information Technology 12(2), 36–41 (2012)CrossRefGoogle Scholar
  11. 11.
    Borzemski, L., Kaminska-Chuchmala, A.: Client-Perceived Web Performance Knowledge Discovery through Turning Bands Method. Cybern. Syst. 43(4), 354–368 (2012)CrossRefGoogle Scholar
  12. 12.
    Borzemski, L., Kaminska-Chuchmala, A.: Distributed Web Systems Performance Forecasting Using Turning Bands Method. IEEE. Trans. Ind. Inform. PP(99), 1, doi:10.1109/TII.2012.2198644, ISSN=1551-3203Google Scholar
  13. 13.
    Matheron, G.: Quelques aspects de la montée. Internal Report N-271, Centre de Morphologie Mathematique, Fontainebleau (1972)Google Scholar
  14. 14.
    Matheron, G.: The intrinsic random functions and their applications. JSTOR Advances in Applied Probability 5, 439–468 (1973)MathSciNetzbMATHCrossRefGoogle Scholar
  15. 15.
    Kaminska-Chuchmala, A., Wilczynski, A.: 3D electric load forecasting using geostatistical simulation method Turning Bands. The works of Wroclaw Scientific Society, series B, XVI(215), 41–48 (2009)Google Scholar
  16. 16.
    Kaminska-Chuchmala, A., Wilczynski, A.: Analysis of different methodological factors on accuracy of spatial electric load forecast performed with Turning Bands method. Rynek Energii 2(87), 54–59 (2010)Google Scholar
  17. 17.
    Lantuejoul, C.: Geostatistical Simulation: Models and Algorithms. Springer (2002)Google Scholar
  18. 18.
    Wackernagel, H.: Multivariate Geostatistics: an Introduction with Applications. Springer, Berlin (2003)zbMATHGoogle Scholar
  19. 19.
    Borzemski, L., Cichocki, Ł., Fraś, M., Kliber, M., Nowak, Z.: MWING: A Multiagent System for Web Site Measurements. In: Nguyen, N.T., Grzech, A., Howlett, R.J., Jain, L.C. (eds.) KES-AMSTA 2007. LNCS (LNAI), vol. 4496, pp. 278–287. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  20. 20.
    Borzemski, L., Cichocki, Ł., Kliber, M.: Architecture of Multiagent Internet Measurement System MWING Release 2. In: Håkansson, A., Nguyen, N.T., Hartung, R.L., Howlett, R.J., Jain, L.C. (eds.) KES-AMSTA 2009. LNCS, vol. 5559, pp. 410–419. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  21. 21.
    Borzemski, L.: The experimental design for data mining to discover web performance issues in a Wide Area Network. Cybern. Syst. 41(1), 31–45 (2010)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Leszek Borzemski
    • 1
  • Michal Danielak
    • 1
  • Anna Kaminska-Chuchmala
    • 1
  1. 1.Institute of InformaticsWroclaw University of TechnologyWroclawPoland

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