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De ne AI t PI on t < Tn rn ; r > 0 Note that AI represents the number of t n I r1 Tn customers in an M=GI =I queue; for that reason AI is usually called an t M =GI =I process An important observation is that this process represents a Poisson limit of a large number of on=off processes Hence, it can be used as a good approximation of an aggregate process obtained by multiplexing a large ( nite) number of on=off processes An important parameter that in many ways determines the uid queue performance is the length of the arrival process activity period Let I I;on be a generic activity period of an M =GI =I process Theorem 10411 The asymptotics of the distribution of I I;on and its integrated tail are related as follows: (i) If F1 P s, then I.

based estimators are essentially optimal computationally speaking, as they have a complexity of only O n , and a direct, nonproblematic implementation that can even be performed in real time [62] Simple estimators of scaling such as the variogram [17] also have excellent computational properties; however, they suffer from signi cant bias and high variance [68] At the other end of the spectrum, fully parametric maximum likelihood estimators require the inversion of an n n autocovariance matrix, an O n3 operation, which is unsuitable for anything but small data sets Even approximate forms such as the Whittle estimator, or discrete versions of it [5, 17], involve numerical minimization and are prohibitively slow for the large n > 214 data sets now routinely encountered in teletraf c studies.

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Statistically, the best performing estimators are fully parametric, such as those based on maximum likelihood, which offer zero bias and optimal variance provided that the data t the chosen parametric model As mentioned above, to avoid extreme computational dif culties encountered for all but small data sets, approximate forms are used in practice, which retain these desirable statistical properties asymptotically [5, 17] Taqqu et al [68, 71], give a comparative discussion of the statistical properties of a variety of estimators of long-range dependence It is shown that approximate maximum likelihood-based estimators such as the Whittle, aggregated Whittle, and local Whittle methods still offer the best statistical performance when compared against alternatives such as the absolute value method, the variance method, the variance of residuals method, the R=S method, and the periodogram method (see also Teverovsky et al [66] and Taqqu and Teverovsky [69, 72]).

We therefore compare against such parametric alternatives Being parametric based, such estimators can make full use of the data and will therefore outperform logscale diagram based estimators, which are constrained to use only those scales where the scaling is both present and apparent Although it is possible that all the data may be accessible to a logscale diagram based estimator that is, that j1 ; j2 can be chosen correctly as j1 ; j2 1; log2 n , this is unlikely in general It is even possible that a data set is too short to contain scales in the scaling range, in which case the logscale diagram based estimators, or indeed any semiparametric estimator of scaling, will be useless On the other hand, in practice typically one cannot know the ``true'' model for the data, and parametric estimators based on the wrong model can yield meaningless results.

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