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FOR M=G=I INPUT PROCESSES Code 39 Full ASCII Generator In VS Using Barcode printer for ASPNET Control to generate 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.

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 suspectedRelated: .

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Since traf c loss is sensitive to long-range dependence in the transient state, how to guarantee transient loss performance in the presence of long-range dependence then oses a signi cant challenge to traf c engineering. The challenge lies mainly in two aspects. First, beyond the region of steady state, marginal state distributions of traf c sources have only limited use. On the other hand, it is not feasible, if not impossible, to use transient state distributions for traf c engineering. Second, in a network, traf c processes will lose any renewal properties that they might possess at the edge of the network, making transient loss performance analysis and guarantee even more dif cult. Transient loss performance guarantee in the presence of longrange dependence may be an important issue in future research. Another related issue is transient call blocking analysis. Due to the same reason that motivated the work presented in this chapter, call blocking performance should also be analyzed in the transient state. Recent studies show that it may be more appropriate to model call duration times in wide-area networks by heavy-tailed distributions [7]. If this is indeed the case, then due to the high variability caused by heavy-tailed distributions, it will take much longer for the system to approach the. Code 128 Code Set C Generator In .NET Using Barcode creator for .Related: 

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BROADCAST VIDEO Code 39 Full ASCII Printer In NET Framework Using Barcode generator for ASP 12, for all multiplexer buffer sizes studied, the mean loss rates of the 10 sample paths fall within the range of losses btained for actual 1% Also, the actual loss rate falls within the range of mean losses for the model 1 standard deviation Hence, the DAR model is a suf ciently accurate model for the lm sequence Frater et al [9] successfully described this source with a simpler model Their scenic model is a generalization of the basic DAR model of Eq (121) that permits the times between changes of state to have any distribution on the positive integers Figure 1213 shows the simulation results for the Isaura 1 sequence This sequence has a mean rate of 149 Mb=s and a peak rate of 33 Mb=s.

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The simulation was done with 8 sources being multiplexed The buffer sizes and the multiplexer input and output rates are the same as those used for lm The output link utilization was 077 The losses resulting from use of the DAR and the Markov chain models are compared to the actual losses As in Fig 1212, the actual losses and the losses resulting from a scaling of the mean rate of the actual sequence by 1% is shown in the gure The losses due to the models are represented using the mean losses from 10 sample paths and the mean 1 standard deviation values From the gure, it can be seen that the DAR model overestimates cell losses and is not an acceptable model for this sequence unless conservative estimates are suf cient However, as can be seen in Fig 12.

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13, the Markov chain model is suf ciently accurate since the mean loss probabilities obtained using the Markov chain model are always lower than the losses obtained for actual rate plus 1% and always higher than the losses obtained forRelated: Generate Codabar NET , Create ITF-14 NET , Print Interleaved 2 of 5 NET.

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