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Seminario del 2009
2009
16 dicembre
Prof. Stefano Bregni (Politecnico di Milano)
Seminario di probabilità
Internet traffic exhibits self-similarity and long-range dependence
(LRD) on various time scales. In this paper, we propose to use the
Modified Allan Variance (MAVAR) and a Modified Hadamard Variance
(MHVAR) to estimate the Hurst parameter H of LRD traffic series or,
more generally, the exponent a of data with fa (a < 0) power-law
spectrum. MHVAR generalizes the principle of MAVAR, a time-domain
quantity widely used for frequency stability characterization, to
higher-order differences of input data. In our knowledge, this MHVAR
has been mentioned in literature only few times and with little
detail so far.
The behaviour of MAVAR and MHVAR with power-law random processes and
some common deterministic signals (viz. drifts, sine waves, steps) is
studied by analysis and simulation. The MAVAR and MHVAR accuracy in
estimating H is evaluated and compared to that of wavelet Logscale
Diagram (LD). Extensive simulations show that MAVAR and MHVAR achieve
significantly better confidence and no bias in H estimation.
Moreover, MAVAR and MHVAR feature a number of other advantages, which
make them valuable to complement other established techniques such as
LD. Finally, MHVAR and LD are also applied to a real IP traffic
trace.