Advertisement

Server Load Prediction on Wikipedia Traffic: Influence of Granularity and Time Window

  • Cláudio A. D. SilvaEmail author
  • Carlos GriloEmail author
  • Catarina SilvaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 942)

Abstract

Server load prediction has different approaches and applications, with the general goal of predicting future load for a period of time ahead on a given system. Depending on the specific goal, different methodologies can be defined. In this paper, we follow a pre-processing approach based on defining and testing time-windows and granularity using linear regression, ANN and SVM learning models. Results on real data from Wikipedia servers show that it is possible to tune the size of the time-window and the granularity to improve prediction results.

Keywords

Load forecasting Linear regression Artificial Neural Networks Support Vector Machines Server load prediction Wikipedia 

References

  1. 1.
    Di Persio, L., Cecchin, A., Cordoni, F.: Novel approaches to the energy load unbalance forecasting in the Italian electricity market. J. Math. Ind. 7, 5 (2017)CrossRefGoogle Scholar
  2. 2.
    Park, D.C., El-Sharkawi, M.A., Marks, R.J., Atlas, L.E., Damborg, M.J.: Electric load forecasting using an artificial neural network. IEEE Trans. Power Syst. 6(2), 442–449 (1991)CrossRefGoogle Scholar
  3. 3.
    Dalrymple, D.J.: Sales forecasting practices: results from a united states survey. Int. J. Forecast. 3(3–4), 379–391 (1987)CrossRefGoogle Scholar
  4. 4.
    Hipni, A., El-shafie, A., Najah, A., Karim, O.A., Hussain, A., Mukhlisin, M.: Daily forecasting of dam water levels: comparing a support vector machine (SVM) model with adaptive neuro fuzzy inference system (ANFIS). Water Resour. Manag. 27(10), 3803–3823 (2013)CrossRefGoogle Scholar
  5. 5.
    Gross, G., Galiana, F.D.: Short-term load forecasting. Proc. IEEE 75(12), 1558–1573 (1987)CrossRefGoogle Scholar
  6. 6.
    Lorido-Botran, T., Miguel-Alonso, J., Lozano, J.A.: A review of auto-scaling techniques for elastic applications in cloud environments. J. Grid Comput. 12(4), 559–592 (2014)CrossRefGoogle Scholar
  7. 7.
    Dinda, P.A.: Online prediction of the running time of tasks. In: 10th IEEE International Symposium on High Performance Distributed Computing. IEEE (2001)Google Scholar
  8. 8.
    Pukach, P., Hladun, P.: Using dynamic neural networks for server load prediction. Comput. Linguist. Intell. Syst. 2, 157–160 (2018)Google Scholar
  9. 9.
    Aljabari, G., Tamimi, H.: Server load prediction based on dynamic neural networks. In: Students Innovation Conference. Palestine Polytechnic University (2012)Google Scholar
  10. 10.
    Ahmed, A., Brown, D.J., Gegov, A.: Dynamic resource allocation through workload prediction for energy efficient computing. In: Advances in Computational Intelligence Systems. Springer, Cham, pp. 35–44 (2017)Google Scholar
  11. 11.
    Herbst, N., Amin, A., Andrzejak, A., Grunske, L., Kounev, S., Mengshoel, O.J., Sundararajan, P.: Online workload forecasting. In: Self-Aware Computing Systems. Springer, Cham, pp. 529–553 (2017)CrossRefGoogle Scholar
  12. 12.
    Caballé, S., Xhafa, F.: Distributed-based massive processing of activity logs for efficient user modeling in a Virtual Campus. Clust. Comput. 16(4), 829–844 (2013)CrossRefGoogle Scholar
  13. 13.
    Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)CrossRefGoogle Scholar
  14. 14.
    Gori, M., Tesi, A.: On the problem of local minima in backpropagation. IEEE Trans. Pattern Anal. Mach. Intell. 1, 76–86 (1992)CrossRefGoogle Scholar
  15. 15.
    Rojas, I., Pomares, H., Valenzuela, O.: Time Series Analysis and Forecasting: Selected Contributions from ITISE 2017. Springer (2017)Google Scholar
  16. 16.
    Farahnakian, F., Liljeberg, P., Plosila, J.: LiRCUP: linear regression-based CPU usage prediction algorithm for live migration of virtual machines in data centers. In: 39th EUROMICRO Conference on Software Engineering and Advanced Applications (SEAA). IEEE (2013)Google Scholar
  17. 17.
    Naseera, S.: A comparative study on CPU load predictions in a computational grid using artificial neural network algorithms. Indian J. Sci. Technol. 8, 35 (2015)CrossRefGoogle Scholar
  18. 18.
    Yu, Y., Zhan, X., Song, J.: Server load prediction based on improved support vector machines. In: 2008 IEEE International Symposium on IT in Medicine and Education (2008)Google Scholar
  19. 19.
    Jain, A., Satish, B.: Clustering based short term load forecasting using support vector machines. In: PowerTech, Bucharest. IEEE (2009)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Technology and ManagementPolytechnic Institute of LeiriaLeiriaPortugal
  2. 2.CIICPolytechnic Institute of LeiriaLeiriaPortugal
  3. 3.Center for Informatics and Systems of the University of CoimbraCoimbraPortugal

Personalised recommendations