On-line Modeling of Non-stationary Network Traffic with Schwarz Information Criterion  

作　　者：夏正敏 陆松年 李建华 铁玲 

机构地区：[1]Department of Electronic Engineering Key Lab of Information Security Integrated Management Research,Shanghai Jiaotong University [2]School of Information Security Engineering Key Lab of Information Security Integrated Management Research,Shanghai Jiaotong University

出　　处：《Journal of Shanghai Jiaotong university(Science)》2010年第2期213-217,共5页上海交通大学学报（英文版）

基　　金：the National High Technology Research and Development Program (863) of China(Nos. 2005AA145110 and 2006AA01Z436);the Natural Science Foundation of Shanghai of China(No. 05ZR14083);the Pudong New Area Technology Innovation Public Service Platform of China(No. PDPT2005-04)

摘　　要：Modeling of network traffic is a fundamental building block of computer science. Measurements of network traffic demonstrate that self-similarity is one of the basic properties of the network traffic possess at large time-scale. This paper investigates the change of non-stationary self-similarity of network traffic over time,and proposes a method of combining the discrete wavelet transform (DWT) and Schwarz information criterion (SIC) to detect change points of self-similarity in network traffic. The traffic is segmented into pieces around changing points with homogenous characteristics for the Hurst parameter,named local Hurst parameter,and then each piece of network traffic is modeled using fractional Gaussian noise (FGN) model with the local Hurst parameter. The presented experimental performance on data set from the Internet Traffic Archive (ITA) demonstrates that the method is more accurate in describing the non-stationary self-similarity of network traffic.Modeling of network traffic is a fundamental building block of computer science. Measurements of network traffic demonstrate that self-similarity is one of the basic properties of the network traffic possess at large time-scale. This paper investigates the change of non-stationary self-similarity of network traffic over time,and proposes a method of combining the discrete wavelet transform （DWT） and Schwarz information criterion （SIC） to detect change points of self-similarity in network traffic. The traffic is segmented into pieces around changing points with homogenous characteristics for the Hurst parameter,named local Hurst parameter,and then each piece of network traffic is modeled using fractional Gaussian noise （FGN） model with the local Hurst parameter. The presented experimental performance on data set from the Internet Traffic Archive （ITA） demonstrates that the method is more accurate in describing the non-stationary self-similarity of network traffic.

关 键 词：network traffic model SELF-SIMILARITY Schwarz information criterion (SIC) discrete wavelet transform (DWT) fractional Gaussian noise (FGN) 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]