On the self-similar nature of Ethernet traffic (extended version)
IEEE/ACM Transactions on Networking (TON)
SIAM Journal on Scientific Computing
Self-similarity and heavy tails: structural modeling of network traffic
A practical guide to heavy tails
A Novel Approach to the Estimation of the Hurst Parameter in Self-Similar Traffic
LCN '02 Proceedings of the 27th Annual IEEE Conference on Local Computer Networks
Self-similar Traffic Prediction Using Least Mean Kurtosis
ITCC '03 Proceedings of the International Conference on Information Technology: Computers and Communications
On the relationship between file sizes, transport protocols, and self-similar network traffic
ICNP '96 Proceedings of the 1996 International Conference on Network Protocols (ICNP '96)
A Model for Self-Similar Ethernet LAN Traffic: Design, Implementation, and Performance Implications
A Model for Self-Similar Ethernet LAN Traffic: Design, Implementation, and Performance Implications
Long-Range Dependence: Ten Years of Internet Traffic Modeling
IEEE Internet Computing
Wavelet analysis of long-range-dependent traffic
IEEE Transactions on Information Theory
Traffic models in broadband networks
IEEE Communications Magazine
Proceedings of the 2nd international conference on Performance evaluation methodologies and tools
The Video Streaming Monitoring in the Next Generation Networks
NEW2AN '09 and ruSMART '09 Proceedings of the 9th International Conference on Smart Spaces and Next Generation Wired/Wireless Networking and Second Conference on Smart Spaces
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Long-range dependent (LRD) self-similar chaotic behaviour has been found to be present in internet traffic by many researchers. The ‘Hurst exponent', H, is used as a measure of the degree of long-range dependence. A variety of techniques exist for estimating the Hurst exponent; these deliver a variable efficacy of estimation. Whilst ways of exploiting these techniques for control and optimization of traffic in real systems are still to be discovered, there is need for a reliable estimator which will characterise the traffic. This paper uses simulation to compare established estimators and introduces a new estimator, HEAF, a ‘Hurst Exponent Autocorrelation Function' estimator. It is demonstrated that HEAF(2), based on the sample autocorrelation of lag2, yields an estimator which behaves well in terms of bias and mean square error, for both fractional Gaussian and FARIMA processes. Properties of various estimators are investigated and HEAF(2) is shown to have promising results. The performance of the estimators is illustrated by experiments with MPEG/Video traces.