An Approach to Dynamic Asymptotic Estimation for Hurst Index of Network Traffic
Xiaoyan MA, Hongguang LI
DOI: 10.4236/ijcns.2010.32023   PDF    HTML     4,771 Downloads   8,312 Views   Citations


As an important parameter to describe the sudden nature of network traffic, Hurst index typically conducts behaviors of both self-similarity and long-range dependence. With the evolution of network traffic over time, more and more data are generated. Hurst index estimation value changes with it, which is strictly consistent with the asymptotic property of long-range dependence. This paper presents an approach towards dynamic asymptotic estimation for Hurst index. Based on the calculations in terms of the incremental part of time series, the algorithm enjoys a considerable reduction in computational complexity. Moreover, the local sudden nature of network traffic can be readily captured by a series of real-time Hurst index estimation values dynamically. The effectiveness and tractability of the proposed approach are demonstrated through the traffic data from OPNET simulations as well as real network, respectively.

Share and Cite:

X. MA and H. LI, "An Approach to Dynamic Asymptotic Estimation for Hurst Index of Network Traffic," International Journal of Communications, Network and System Sciences, Vol. 3 No. 2, 2010, pp. 167-172. doi: 10.4236/ijcns.2010.32023.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] K. Park and W. Willinger, “Self-similar network traffic and performance evaluation,” Wiley-Interscience, New York, 2000.
[2] S. Y. Yin and X. K. Lin, “Traffic self-similarity in mobile ad hoc networks,” in Proceedings of Second IFIP International Conference Wireless and Optical Communi- cations Networks, pp. 285–289, 2005.
[3] A. Athanasopoulos, E. ToPalis, C. D. Antonopoulos et al., “Evaluation analysis of the permance of IEEE802.1lb and IEEE802.llg standards,” in Proceedings of International Conference Networking, International Conference on Systems and International Conference on Mobile Com- munications and Leaning Technologies, available at:, 2006.
[4] M. Jiang, M. Nikolic, S. Hardy et al. “Impact of self-similarity on wireless data network Performance,” ICC 2001, IEEE International Conference Communi- cations, Vol. 2, No. 11–14, pp. 477–481, 2001.
[5] J. S. Zh., H. Ming, and N. B. Shroff, “Sudden data over CDMA: MAI self-similarity, rate control and admission control and admission control,” in proceedings of IEEE INFOCOM 2002, Vol. 1, No. 23–27, pp. 391–399, 2002.
[6] R. Kalden and S. Ibrahim, “Searching for self-similarity in GPRS[C],” The 5th Annual Passive & Active Measurement Workshop, PAM 2004, France, April 2004.
[7] Muradtaqqu. Methods. /murad/ methods/index.html, September 2005.
[8] J. W. Wei, J. Zhang, and J. X. Wu, “A long-range dependence sliding window time-varying estimation algorithm for network traffic,” Journal of Computer Research and Development, Vol. 45, No. 3, pp. 436–442, 2008.
[9] O. Cappe, E. Moulines, A. PetroPulu et al., “Long-range dependence and heavy-tail modeling for teletraffic data,” IEEE Signal Processing Magazine, Special Issue on Analysis and Modeling of High-Speed Data Network Traffic,” Vol. 19, No. 5, pp. 14–27, May 2002.
[10] D. R. Figueiredo, B. Liu, V. Misra, and D. Towsley, “On the autocorrelation structure of TCP traffic, Computer Networks, Vol. 40, No. 3, pp. 339–361, 2002.
[11] D. R. Figueiredo, B. Y. Liu, A. Feldmann, V. Misra, D. Towsley, and W. Willinger, “On TCP and self-similar traffic,” Performance Evaluation, 2005.
[12] P. Danzig, J. Mogul, and V. Paxaon, “Traces available in the interact traffic archive,” /tmces.html. September 2005.
[13] W. E. Leland, M. S. Taqqu, and W. Willinger, et a1. “On the self-similar nature of ethernet traffic,” IEEE/ACM Transactions on Networking, Vol. 2, No. 1, pp. l–15. 1994.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.