Analysis of the Self-Similar Characteristics of Broadband Traffic in the Wavelet Domain
In this paper we present a wavelet-based method for the analysis of data traffic exhibiting Long Range Dependence (LRD). A key element in determining network performances is the bursty nature of real traffic patterns and the estimation of the Hurst parameter H, a measure of the long term correlation level, represents a major topic in network dimensioning and management. The goal of this paper consists in analysing the statistical properties of measured traffic streams in the framework of the wavelet decomposition, not only to provide an efficient algorithm for the estimation of H, but also to investigate their behaviour at different time-scales.
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