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traf c processes considered, we observe that as the initial value varies, the transient loss measures will become greater than the corresponding steady-state loss measures That is, steady-state loss measures can underestimate actual loss in the transient state As the buffer size B or the service rate C increases, we can still observe such underestimation Although increasing C can decrease the probability for w to take larger values, due to the randomness of on and off periods, it is still possible for w to take values greater than some critical value beyond which the steady-state loss measures will underestimate transient loss We even observe that if C (or B) is large enough, then for all w P 0; B , the steady-state loss measures are strictly less than the corresponding transient loss measures; for example, see the case of C 0:9 in Fig 133(d) 13.



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5 THE IMPACT OF LONG-RANGE DEPENDENCE IN NETWORK TRAFFIC If bandwidth allocated to a traf c source equals the peak bit rate of the source, then whether there exists long-range dependence in the underlying traf c has no impact at all on traf c modeling and engineering So in the following discussions, we assume that the bandwidth allocated to an LRD traf c source is less than its peak rate First of all, perhaps the most important issue is whether long-range dependence is relevant to traf c engineering [6, 9, 10, 16, 17] It has been shown that in the steady state, when the buffer size is small, long-range dependence may not have signi cant impact [10, 17] However, the above conclusion is drawn in the steady state, so it may not be necessarily valid beyond the region of steady state.

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Our analysis and numerical results have shown that, in the transient state, there exists signi cant difference in traf c loss behavior between traditional Markov models and LRD models Analysis results presented in Section 133 describe a scenario regarding transient loss performance impact of long-range dependence in multistate uid traf c In nontrivial cases, after the uid enters a state, it will pour more traf c into the buffer than what the bandwidth can transmit However, according to Lemma 1331, traf c loss will not take place until the time spent by the uid in the state exceeds the value of t w , which is the instant after which loss of traf c begins From Theorems 1336 and 133.

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7, we see that there exists some critical value of t w and y determined only by the property of the underlying LRD traf c, beyond which Markov models can underestimate loss performance degradation for LRD traf c Since large values of t w and y imply stringent loss performance requirements, the above results in fact say that, in the transient state, long-range dependence is more likely to have signi cant impact on applications with stringent loss constraints Although a small buffer may alleviate the impact of long-range dependence in the steady state [9, 10], it does not help in the transient state if the loss constraint is stringent Let us take the conditional loss probability as an example Since t w B w = r C depending on buffer and bandwidth allocated to the uid, it can be viewed as a control variable From Lemma 133.

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