Non-stationarity and high-order scaling in TCP flow arrivals: a methodological analysis

The last decade has been a very fruitful period in important discoveries in network traffic modeling, uncovering various scaling behaviors. Self-similarity, long-range dependence, multifractal behavior and finally cascades have been studied and convincingly matched to real traffic. The first purpose of this paper is to provide a methodology to go beyond the naive analysis of the second-order wavelet-based estimators of scaling, by performing non-stationarity checks and relying on the information contained in the high-order properties of the wavelet coefficients. Then, we apply this methodology to study the scaling properties of the TCP flow arrivals based on several traffic traces spanning the years from 1993 to early 2002. Our study reveals that the second-order scaling properties of this process describe its dynamics quite well. However, our analysis also provides evidence that high-order scaling in this process appears due to pathological behaviors like rate limitation and nonstationarity