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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.

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: Print QR Code Word Image, Printing QR Code NET WinForms Size, QR Code Printing ASPNET Data.

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Using Barcode printer for ASPNET Control to generate, create Code 39 Full ASCII image in ASPNET In this chapter, we present an analysis of transient loss performance impact of long-range dependence in etwork traf c This work is only the rst step of our exploration But we hope that it will still be helpful for understanding loss performance impact of long-range dependence in the transient state, although much further work needs to be done in the future A different transient analysis in the context of capacity planning and recovery time is given in 17 In general, the transient analysis of queueing models is very challenging A transient solution to a queueing model is a function of a time index, either continuous or discrete, de ned over an in nite range It is very dif cult to nd an explicit, closed-form solution if the system is not Markovian.

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For a Markov model, the probabilistic evolution of the system state is governed by the Chapman Kolmogorov equation In the presence of long-range dependence, the model is essentially non-Markovian So the Chapman Kolmogorov equation does not hold anymore Due to this and other dif culties involved in transient analysis, most of the existing work on QoS impact of long-range dependence is limited to asymptotic analysis in the steady state [4, 6, 9, 10, 15 17] (see also s 4 10 in this volume) Although certain insights have been gained through investigation carried out in steady-state, we feel that it is still necessary to extend the investigation beyond the region of steady-state First, due to the high variability caused by long-range dependence, the convergence of an LRD traf c process toward steady-state can be very slow In contrast, Markov traf c processes converge to steady-state exponentially fast.

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Consequently, for a link carrying LRD traf c, the discrepany between steady-state performance and transient performance can be more signi cant, compared with the situations in which traf c can be modeled by Markov processes Second, to guarantee QoS for traf c with the high variability caused by long-range dependence, dynamic and adaptive resource allocation may be necessary to account for the effect of the current system state Performance analysis based on the steady state may not be appropriate for this purpose, since in steady-state, any initial effect will disappear eventually Because steady-state performance may differ from transient performance signi cantly for LRD traf c, the image regarding loss behavior of LRD traf c in the transient state still largely remains vague.

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In this chapter, an approach different from conventional transient analysis is used, which allows us to investigate the transient performance impact of long-range dependence in traf c without rst seeking a closed-form transient solution That is, we limit our analysis to some short period of time, and even to a single state of a traf c process The reasons for us to adopt this approach are as follows First, it is relatively easy, and may also be suf cient, to consider transient solutions de ned only for a relatively short time period, since a short time period may actually cover the time span in which we are interested for transient performance analysis A large time span may be less interesting from a point of view of transient analysis, since the difference between the steady state and the transient state may diminish signi cantly after a long time has elapsed.

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