On the self-similar nature of Ethernet traffic (extended version)
IEEE/ACM Transactions on Networking (TON)
A Case for Exploiting Self-Similarity of Network Traffic in TCP
ICNP '02 Proceedings of the 10th IEEE International Conference on Network Protocols
On-chip traffic modeling and synthesis for MPEG-2 video applications
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
A statistical test for the time constancy of scaling exponents
IEEE Transactions on Signal Processing
A wavelet-based joint estimator of the parameters of long-range dependence
IEEE Transactions on Information Theory
A practical method for weak stationarity test of network traffic with long-range dependence
MUSP'08 Proceedings of the 8th WSEAS International Conference on Multimedia systems and signal processing
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Network traffic exhibits fractal characteristics, such as self-similarity and long-range dependence. Traffic fractality and its associated burstiness have important consequences for the performance of computer networks, such as higher queue delays and losses than predicted by classical models. There are several estimators of the fractal parameters, and those based on the discrete wavelet transform (DWT) are the best in terms of efficiency and accuracy. The DWT estimator does not consider the possibility of changes to the fractal parameters over time. We propose using the Schwarz information criterion (SIC) to detect changes in the variance structure of the wavelet decomposition and then segmenting the trace into pieces with homogeneous characteristics for the Hurst parameter. The procedure can be extended to the stationary wavelet transform (SWT), a non-orthogonal transform that provides higher accuracy in the estimation of the change points. The SIC analysis can be performed progressively. The DWT-SIC and SWT-SIC algorithms were tested against synthetic and well-known real traffic traces, with promising results.