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Predictive Models of Network Traffic Load

  • Federico Montesino Pouzols
  • Diego R. Lopez
  • Angel Barriga Barros
Part of the Studies in Computational Intelligence book series (SCI, volume 342)

Abstract

Understanding the dynamics and performance of packet switched networks on the basis of measurements enables practitioners to optimize resources. As network measurement research further advances and new measurement tools and infrastructures are available, the task of network operation becomes more and more complex. In this chapter we apply the methodology developed in the previous chapter to time series concerning network traffic load. An extensive predictability analysis is performed using the same nonparametric residual variance estimation technique that is integrated into the prediction methodology. Based on the predictability results, fuzzy inference based models that are both interpretable and accurate are derived for a wide set of heterogeneous time series for network traffic.

Keywords

Fuzzy Inference System Test Error ARIMA Model Digital Equipment Corporation Time Series Prediction 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Federico Montesino Pouzols
    • Diego R. Lopez
      • Angel Barriga Barros

        There are no affiliations available

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