In this work we point out that some common methods for estimating self-similarity parameters – involving packet counting for the estimate of statistical moments – are affected by distortion at the finest resolutions and quantization errors and we illustrate – using also a small sample of the Bellcore data set – a technique for removing this undesirable effect,based on factorial moments and strip integrals. Then we extend the strip-integral approach to the approximation of the square of the Haar wavelet coefficients, for the estimate of the Hurst self-affinity exponent.

Poisson-noise removal in self-similarity studies based on packet-counting : factorial-moment/strip-integral approach / G. Gianini, E. Damiani. - In: PERFORMANCE EVALUATION REVIEW. - ISSN 0163-5999. - 35:2(2007), pp. 3-5. [10.1145/1330555.1330559]

Poisson-noise removal in self-similarity studies based on packet-counting : factorial-moment/strip-integral approach

G. Gianini
Primo
;
E. Damiani
Ultimo
2007

Abstract

In this work we point out that some common methods for estimating self-similarity parameters – involving packet counting for the estimate of statistical moments – are affected by distortion at the finest resolutions and quantization errors and we illustrate – using also a small sample of the Bellcore data set – a technique for removing this undesirable effect,based on factorial moments and strip integrals. Then we extend the strip-integral approach to the approximation of the square of the Haar wavelet coefficients, for the estimate of the Hurst self-affinity exponent.
Network traffic ; Factorial moments ; Strip integral ; Self-similarity ; Haar wavelet ; Hurst exponent.
Settore INF/01 - Informatica
2007
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/41113
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