Generating LRD traffic traces using bootstrapping

Authors:
Shubhankar Chatterjee;Mike MacGregor;Stephen Bates
Affiliations:
Department of Electrical and Computer Engineering, University of Alberta;Department of Computing Science, University of Alberta;Department of Electrical and Computer Engineering, University of Alberta
Venue:
ITC20'07 Proceedings of the 20th international teletraffic conference on Managing traffic performance in converged networks
Year:
2007

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Abstract

Long-range dependence (LRD) or second-order self-similarity has been found to be an ubiquitous feature of internet traffic. In addition, several traffic data sets have been shown to possess multifractal behavior. In this paper, we present an algorithm to generate traffic traces that match the LRD and multifractal properties of the parent trace. Our algorithm is based on the decorrelating properties of the discrete wavelet transform (DWT) and the power of stationary bootstrap algorithm. To evaluate our algorithm we use multiple synthetic and real data sets and demonstrate its accuracy in providing a close match to the LRD, multifractal properties and queueing behavior of the parent data set. We compare our algorithm with the traditional fractional gaussian noise (FGN) model and the more recent multifractal wavelet model (MWM) and establish that it outperforms both these models in matching real data.