Ten lectures on wavelets
On the self-similar nature of Ethernet traffic (extended version)
IEEE/ACM Transactions on Networking (TON)
IEEE/ACM Transactions on Networking (TON)
SIAM Journal on Scientific Computing
Connection-level analysis and modeling of network traffic
IMW '01 Proceedings of the 1st ACM SIGCOMM Workshop on Internet Measurement
Estimation of the self-similarity parameter in linear fractional stable motion
Signal Processing - Signal processing with heavy-tailed models
Estimation of the self-similarity parameter using the wavelet transform
Signal Processing
Non-stationarity and high-order scaling in TCP flow arrivals: a methodological analysis
ACM SIGCOMM Computer Communication Review
Long-range dependence in a changing internet traffic mix
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Long range dependent trafic
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Long range dependent trafic
LASS: a tool for the local analysis of self-similarity
Computational Statistics & Data Analysis
Wavelet analysis of long-range-dependent traffic
IEEE Transactions on Information Theory
A wavelet-based joint estimator of the parameters of long-range dependence
IEEE Transactions on Information Theory
Self-similarity and long-range dependence in teletraffic
MUSP'09 Proceedings of the 9th WSEAS international conference on Multimedia systems & signal processing
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This article studies the problem of estimating the self-similarity parameter of network traffic traces. A robust wavelet-based procedure is proposed for this estimation task of deriving estimates that are less sensitive to some commonly encountered non-stationary traffic conditions, such as sudden level shifts and breaks. Two main ingredients of the proposed procedure are: (i) the application of a robust regression technique for estimating the parameter from the wavelet coefficients of the traces, and (ii) the proposal of an automatic level shift removal algorithm for removing sudden jumps in the traces. Simulation experiments are conducted to compare the proposed estimator with existing wavelet-based estimators. The proposed estimator is also applied to real traces obtained from the Abilene Backbone Network and a university campus network. Both results from simulated experiments and real trace applications suggest that the proposed estimator is superior.