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^ Fig. 15.9 Con dence intervals for l2 for the tted Weibull, lognormal, exponential, and Pareto models of all interarrival times for the eight datasets.

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^ ^ corresponds to the value of l2 and the error bars to the standard deviation of l2 or the 68.26% con dence intervals. We see that for our datasets the Weibull model does substantially better than the others (except for FTP.DATA, consistent with Paxson and Floyd [35]) although the value of the discrepancy is quite substantial for some of the application types and some datasets. In fact, for all CMU datasets the Weibull model is statistically better than the other models in 203 of 240 cases, it is indistinguishable in 25 cases, and is beaten by one of the other models in only 12 instances. Actually the Pareto model is better in eight cases, while the exponential model is better in three cases, and the lognormal model in one case. For the AT&T datasets the Weibull model is statistically better than the other models in 90 of 95 instances, it is indistinguishable from the Pareto or the exponential model in three instances, and it is beaten by the Pareto model in only two instances. For the LBL and DEC datasets we observe that the Weibull distribution is capable of modeling some but not all applications. For example, the Weibull distribution performs very well for SMTP, NNTP, and FTP.CONN connections but poorly for HTTP and TELNET connections for the 1993 LBL-7 dataset. An explanation for HTTP is that very few people were using it at the time of the collection of the trace. Therefore the sample may not be appropriate. For TELNET it seems that a nonstationary approach is

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