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service time also yields Weibull-like buffer asymptotics although the input process is now short-range dependent Hence, the same asymptotic buffer behavior can be induced by two vastly different input streams, one long-range dependent and the other short-range dependent! To make matters worse, if the pmf G were Pareto instead of Weibull, the input process would now be long-range dependent, in fact asymptotically self-similar [28], but the buffer distribution would now exhibit Pareto-like asymptotics, in sharp contrast with the results of Norros [25] To reiterate the main conclusion of Parulekar and Makowski [28], the value of the Hurst parameter as the sole indicator of long-range dependence (via asymptotic selfsimilarity) is at best questionable as it does not characterize buffer asymptotics by itself.
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Furthermore, buffer sizing cannot be determined adequately by appealing solely to the short- versus long-range dependence characterization of the input model used, be it of the M =G=I type or otherwise Of course, this is not too surprising since long-range dependence (and its close cousin, second-order selfsimilarity) is determined by second-order properties of the input process, while asymptotics of the form (91) invoke much ner probabilistic properties, which are embedded here in the sequence fv*; t 1; 2; g The niteness of E s2 (which t characterizes the SRD nature of the M =G=I input process) is obviously a poor marker for predicting the behavior of this sequence To close, we note that the diverse queueing behavior demonstrated here is tied to the tail behavior of s, which determines the correlation structure of M=G=I inputs.
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This clearly illustrates the tremendous impact that the correlation structure of an input stream can have on the corresponding queueing performance given that the M =G=I inputs all have Poisson marginals! One more data point for the need of a cautious approach in modeling network traf c when time dependencies are either observed or suspected..
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