One Method from LRD to SRD

Research of self-similarity has been the focus of flow prediction due to the complexity of self-similar traffic, but the LRD (long range dependence) traffic has the disadvantages of the high modeling algorithm complexity and poor precision. Therefore we tried to propose a way that can make the LRD traffic change to SRD (short range dependence) which is more simple than LRD in the field of modeling and predicting. The researchers adopted EMD (Empirical Mode Decomposition) to decompose LRD data which would be decomposed into several IMF (Intrinsic Mode Function) components. Then we found that IMF components had no longer self-similar property through theoretical analysis and simulation, thus people could use some SRD model to forecast traffic.

[1]  Patrick Flandrin,et al.  Wavelet analysis and synthesis of fractional Brownian motion , 1992, IEEE Trans. Inf. Theory.

[2]  Stefano Giordano,et al.  On the implications of the off periods distribution in two-state traffic models , 1999, IEEE Communications Letters.

[4]  Zhengyou He,et al.  WNN-based NGN traffic prediction , 2005, Proceedings Autonomous Decentralized Systems, 2005. ISADS 2005..

[5]  Julio Cesar Ramírez Pacheco,et al.  Performance Analysis of Time-domain Algorithms for Self-similar Traffi , 2006, CONIELECOMP.

[6]  A.H. Tewfik,et al.  Correlation structure of the discrete wavelet coefficients of fractional Brownian motion , 1992, IEEE Trans. Inf. Theory.

[7]  Wang Shenghui and Qiu Zhengding Multi-Step Prediction of VBR Video Traffic Based on Mutifractal Analysis , 2007 .

[8]  Fei Xue,et al.  Traffic modeling based on FARIMA models , 1999, Engineering Solutions for the Next Millennium. 1999 IEEE Canadian Conference on Electrical and Computer Engineering (Cat. No.99TH8411).

[9]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[10]  Richard G. Baraniuk,et al.  A Multifractal Wavelet Model with Application to Network Traffic , 1999, IEEE Trans. Inf. Theory.

[11]  Gang Wu,et al.  Applications of nonlinear prediction methods to the Internet traffic , 2001, ISCAS 2001. The 2001 IEEE International Symposium on Circuits and Systems (Cat. No.01CH37196).

[12]  A. Banakar,et al.  Generalized Wavelet Neuro-Fuzzy Model and its Application in Time Series Forecasting , 2006, 2006 International Symposium on Evolving Fuzzy Systems.

[13]  Tho Le-Ngoc,et al.  MMPP modeling of aggregated ATM traffic , 1998, Conference Proceedings. IEEE Canadian Conference on Electrical and Computer Engineering (Cat. No.98TH8341).

[14]  J.M. Fernandez,et al.  Queueing performance comparison of traffic models for Internet traffic , 1998, IEEE GLOBECOM 1998 (Cat. NO. 98CH36250).

[15]  Qiu Zhengding,et al.  Multifractal Analysis and Prediction of VBR Video Traffic , 2006, 2006 6th International Conference on ITS Telecommunications.

[16]  Walter Willinger,et al.  On the self-similar nature of Ethernet traffic , 1993, SIGCOMM '93.