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distributional and independence assumptions listed above Eq. (12.5) are valid, then the GBAR process will be formed. Simulating the GBAR process only requires the ability to simulate independent and identically distributed gamma and beta random variables. This is easily done; for example, algorithms and Fortran programs are presented in Bratley et al. [3]. The GBAR process is used as a source model by generating noninteger values from Eq. (12.5) and then rounding to the nearest integer. It would be cleaner if a discrete process with negative-binomial marginals could be generated in the rst place. McKenzie describes such a process (his Eq. (3.6)). Unfortunately, that process requires much more computation to simulate, and the extra effort does not appear to be worthwhile. 12.2.3.1 Validating the GBAR Model Ten sample paths of the GBAR process were generated and used as the arrival process (number of cells per frame with a xed interframe time) in a simulation of a service system with a nite buffer and a constant-rate server. The cell-loss rates from these paths were averaged to obtain a point estimate for the GBAR model. The traf c intensity is varied by changing the service rate. The points produced by the simulations are denoted by an asterisk. In Fig. 12.3, we see that cell-loss rates computed from the GBAR model are close to the cell-loss rates computed from the data. Note that for each traf c intensity, the decrease in the cell-loss rate as the buffer size increases is very slight, for both the model and the data. This con rms the prediction of Hwang and Li [19] of buffer ineffectiveness. Since the GBAR model has only short-range dependence, this effect is not caused by long-range dependence here.



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Figure 12.4 shows that the mean queue lengths in an in nite buffer computed from ve GBAR paths are similar to the mean queue lengths computed using the data. In Fig. 12.4 the vertical axis on the left shows the mean queue length in cells. Video quality is poor when the cell delays are large; 100 ms is an upper bound on the acceptable delay at a node in a network that provides video services. Two buffer drain times are also shown; the practical region for the maximum is below and to the left of the 100 ms line. The range of the mean queue lengths shown exceeds the practical region for maximum queue length. In the practical region, the model and the trace give very similar mean delays. The differences between the mean queue length from the GBAR model and the mean queue lengths from the data would be even smaller if a nite buffer were imposed. (This is the truncating effects of nite buffers that is described in Section 12.4.2). The comparisons for sequences B, C, and D are qualitatively the same as for sequence A.

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Whether the Fibre Channel is arranged in a loop, a fabric, or a combination of the two, it still functions as a network. Since SAN technology is relatively new and standards have not been widely adopted, the components can be extremely expensive, although prices have begun to drop in recent times. We have seen cases where the cost of a host bus adapter (HBA, the card that plugs into a server allowing it to connect to a SAN) costs more than the whole server. The networking components that sit on the SAN, including hubs and switches, can also be prohibitively expensive. Costs will continue to fall over the next few years, but in the meantime, SANs will continue to be a significant expense for most organizations.

Measuring message delivery in social media is a lot like measuring it in classic advertising venues, so classic metrics apply. Awareness, reach, and frequency are necessary to determine if your message is getting out there. Yes, there s a twist. You ve reached a large number of people. That s great! But it s a small part of the story. Success in social media is not found in how many people got your message; it s found in how many people thought your message was remarkable literally. How many people were intrigued enough by the point you were making to comment on it and pass it along to their friends This is word of mouth like you ve never seen before, so be prepared to deal with a yardstick that has become articulated and multidimensional.

Now we turn to more dynamic sequences, such as lms, news, sports, and entertainment television. Since the main purpose of the models is to aid in network performance evaluations, we are particularly interested in using the models to predict cell-loss rates.

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