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size in certain buffer regions can decrease the over ow probabilities by orders of magnitude. Formally, for each B > 0 construct a sequence of truncated random variables AB min An1 B : n Next, consider a single server queue with the arrival process fAB ; AB ; n ! 0g, that is, n QB QB AB Cn 1 : n 1 n n 1 Assume that for all B, QB is in its stationary regime. n Theorem 10.3.6. If E A C < 0, for all n > 0, P C > x e Zx ; Z > 0, and A has a regularly varying distribution P A > x l x =xa , then P QB > k d B hk d l B k 1 1 o 1 B k 1 a 1 as B 3 I; 10:6 10:5



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HEURISTIC 10.3.7. In order that the queue exceeds a large buffer size b k d B it is needed that exactly k 1 large arrivals (of the order B) occur at approximately the same time. Since successive arrivals are independent this event is of the order l B k 1 =B k 1 a 1 . The detailed proof of this result can be found in Jelenkovic [32]. REMARK 10.3.8. (i) This result is related to Proposition 1 in Resnick and Samorodnitsky [50], where, under conditions similar to our theorem, a rough bound for the queue length increment during an activity period of the M =GI =I arrival process was derived. (ii) Note that h0 d is explicitly given by h0 d 1 1 da 1 : a 1 da 1 10:8

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Now, we illustrate Theorem 10.3.6 with the following example (for more examples see Jelenkovic [32, 34]). Example 10.3.9. Parameterize the distribution of AB as aB 1 p, aB pd=ia 1 , n i 0 P 1 i B 1, aB 1 B 1 ai , where d 1=z a 1 and z x is a Zeta funcB i 0 tion. For the choice of arrival parameters B 300, a 2:8, and p 0:3 we compute d 1=z a 1 0:273345, aB 0:7, aB 0:0820=ia 1 , 1 i B 1, rB i 0 0:34086. For these values we numerically invert the z-transform of the queue

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stationary process X Xk : k ! 1 is called asymptotically self-similar (with selfsimilarity parameter H, 0 < H < 1), if for all large enough m, X % m1 H X m ; where X m X m k : k ! 1 is the aggregated process of order m, given by X m k 1 X Xkm ; m k 1 m 1 k ! 1:

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The process under consideration is the number of connection arrivals per time unit In the past [5, 7, 20, 27, 35, 41] various graphical tools, such as ``variance time plots,'' ``pox plots of R=S,'' and ``periodogram plots,'' and statistical tools, such as ``periodogram-based MLE estimate,'' have been used to estimate the Hurst parameter Using these tools on the HTTP connection arrival from the busy hour of the ^ external AT&T dataset from March, the estimates are H 0:749 for the variance ^ ^ time plot; H 0:764 for the pox plot of R=S; H 0:796 for the periodogram plot ^ (not restricted to the lower frequencies); H 0:737 with a 95% con dence interval from 0715 to 0758 for the MLE Whittle estimate based on the periodogram Similar estimates have been derived for other subsets of the traces.

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