Estimation of network traffic hurst parameter using HHT and wavelet transform

  • Authors:

  • Xiaorong Cheng;Kun Xie;Dong Wang

  • Affiliations:

  • School of Computer Science and Technology, North China Electric Power University, Baoding, China;School of Computer Science and Technology, North China Electric Power University, Baoding, China;School of Computer Science and Technology, North China Electric Power University, Baoding, China

  • Venue:
  • WiCOM'09 Proceedings of the 5th International Conference on Wireless communications, networking and mobile computing
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

It has been demonstrated that both wide and local area network traffic are statistically self-similar. The Hurst index is the only parameter to characterize the self-similarity. The real-time normal network data stream should accord with network traffic's statistical self-similarity and its long-range dependence (LRD) features correspondingly, which can be judged by the value of Hurst parameter. Wavelet transform is a common method used to estimate self-similar parameter. However, the wavelet analyses can not eliminate the influence of non-stationary signal's periodicity and trend term. In view of the fact that Hilbert-Huang Transform (HHT) has unique advantage on non-stationary signal treatment, a refined self-similar parameter estimation algorithm is designed in this paper through the combination of wavelet analysis and Hilbert-Huang Transform and a set of experiments are run to verify the improvement in the accuracy of parameter estimation.