A new hybrid network traffic prediction method

Lin Xiang, Xiao H. Ge, Chuang Liu, Lei Shu, Cheng Xiang Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

How to predict the self-similar network traffic with high burstiness is a great challenge for network management. The covariation orthogonal prediction could effectively capture the burstiness in the network traffic, and the artificial neural network prediction could adapt the network traffic change by self-learning. To improve the prediction accuracy, we propose a new hybrid network traffic prediction method based on the combination of the covariation orthogonal prediction and the artificial neural network prediction. Through empirical study, the accuracy of the new prediction method can be effectively improved seen from the mean and the prediction error. ©2010 IEEE.

Original languageEnglish
Title of host publication2010 IEEE Global Telecommunications Conference, GLOBECOM 2010
DOIs
Publication statusPublished - 2010
Event53rd IEEE Global Communications Conference 2010 - Miami, FL, United States
Duration: 6 Dec 201010 Dec 2010

Conference

Conference53rd IEEE Global Communications Conference 2010
Abbreviated titleGLOBECOM 2010
CountryUnited States
CityMiami, FL
Period6/12/1010/12/10

Keywords

  • Artificial Neural Networks (ANNs)
  • Burstiness
  • Covariation Orthogonal (CO)
  • Self-similarity
  • Traffic prediction

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  • Cite this

    Xiang, L., Ge, X. H., Liu, C., Shu, L., & Wang, C. X. (2010). A new hybrid network traffic prediction method. In 2010 IEEE Global Telecommunications Conference, GLOBECOM 2010 https://doi.org/10.1109/GLOCOM.2010.5684249