Research ArticleOpen AccessGoogle Scholar indexed
Another Important Parameter’s Research on Estimating Self-Similarity
College of Electronical Information Engineering, West China Normal University, Nanchong, China
The School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China
- 1 College of Electronical Information Engineering, West China Normal University, Nanchong, China
- 2 The School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China
International Journal of Communications, Network and System Sciences·Volume 09 (2016)·Pages 338–345·Published 5 August 2016·DOI10.4236/ijcns.2016.98030
Copy link · social · email
Abstract
It is convincingly demonstrated by numerous studies that the self-similarity of modern multimedia network traffic is presented by Hurst parameter (H). The specific performance is that the similar degree is higher along with the increase of H when H is between 0.5 and 1. However, it is doubtable that whether the complicated process of self-similarity can be described comprehensively by the parameter H only. Therefore, another important parameter cf has been proposed based on the discrete wavelet decomposition in this paper. The significance of the parameters is provided and the performance of the self-similarity process is described better.
KeywordsSelf-SimilarityHurst ParameterParameter cfLong-Rang Dependence
- Leland, W.E., Taqqu, M.S., Winlinger, W. and Wilson, D.V. (1994) The Self-Similar Nature of Ethernet Traffic (Extend Versions). IEEE/ACM Transactions on Network, 2, 1-15. http://dx.doi.org/10.1109/90.282603
- Tyralis, H. and Koutsoyiannis, D. (2010) Simultaneous Estimation of the Parameters of the Hurst-Kolmogorov Stochastic Process. Springer-Verlag, 25, 21-33.
- Abry, P. and Veitch, D. (1998) Wavelet Analysis of Long-Range Dependent Traffic. IEEE Transactions on Information Theory, 44, 2-15. http://dx.doi.org/10.1109/18.650984
- Sanhinoglu, Z. and Tekinary, S. (1999) Multiresolution Decomposition and Burstiness Analysis of Traffic Traces. Wireless Communications and Networking Conference, WCNC, New Orleans, 2, 560-562.
- Abry, P., Goncalves, P. and Flandrin, P. (1993) Wavelet-Based Spectral Analysis of 1/F Process. IEEE International Conference on Acoustics, Speech and Signal Processing, Minneapolis, 3, 237-240.
- Xu, L., Liu, J.K. and Li, L. (2013) Estimation Method of Hurst Exponent of Self-Similarity Network Traffic. Science Technology and Engineering, 13, 5849-5854.
- Abry, P. and Veitch, D. (1999) A Wavelet-Based Joint Estimator of the Parameters of Long-Range Dependence. IEEE/ACM Transactions on Information Theory, 45, 878-897.
- Wu, Y.M., Huang, H. and Li, L.M. (2006) Study on Wavelet Detection Method of HURST Parameters of Self-Similar Traffic Flow. Journal of University of Electronic Science and Technology of China, 22, 354-356.
- Brichet, F., Roberts, J., Simonian, A. and Veitch, D. (1996) Heavy Traffic Analysis of a Storage Model with Long Range Dependent on/off Sources. Queuing Systems, 23,197-210. http://dx.doi.org/10.1007/BF01206557
- Pan, C.S. and Zhao, Y.T. (2013) Research on Frame Multiplexing Efficiency of High Level on Orbit System under Long Correlation Traffic. Journal of System Simulation, 25, 1130-1134.
- Kettani, H. and Gubner, J.A. (2006) A Novel Approach to the Estimation of the Long-Range Dependence Parameter. IEEE Transactions on Circuits and Systems, 53, 463-467. http://dx.doi.org/10.1109/TCSII.2006.873828
- Yu, Q., Mao, Y.M. and Leng, S.P. (2014) The Analysis and Control Technology of Data Network Traffic Modeling. Master Dissertation, Tsinghua University Press, Beijing.
- Wu, Y. and Li, J.Z. (2013) Hurst Parameter Estimation Method Based on Haar Wavelet And Maximum Likelihood Estimation. Journal of Huazhong Normal University, 47, 763-768.