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Network support for variable bit rate (VBR) video needs to consider (1) properties of workload induced (e.g., signi cant autocorrelations into far lags and heterogeneous marginal distributions), and (2) application-speci c bounds on delay-jitter and statistical cell-loss probabilities. This chapter presents a quality-of-service (QoS) solution for such traf c at each multiplexing point in a network. Heterogeneity in both-offered workload and quality-of-service requirements are considered. The network is assumed to be cell-switched with virtual circuits (VCs) similar to that in ATM networks. 16 discusses an alternative approach for provisioning for long-range-dependent (LRD) traf c. See also the work of Heyman and Lakshman ( 12) and Li and Li ( 13). 19.1.2 Correlated Traf c and Its Implications



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All documented procedures must be tested. In an emergency, it is likely that written procedures will be followed blindly. If they don t work exactly right, the result may well be chaos. The only thing worse than no documentation is bad documentation. Since it is virtually impossible to target documentation at inexperienced personnel, the target audience should be experienced system administrators who are not familiar with the particular idiosyncrasies of your local systems and environment. When testing your standards implementation, make sure that systems and applications meet the documented local standards. Know what all test results should be before the test is run. Test your backups by restoring a couple of them on a regular basis. If you cannot restore your backups in a test environment, you won t be able to restore them when a disaster occurs. Test any critical process whose failure may cause downtime. Consider less obvious potential SPOFs, like building access scanners or locked doors.

Studies on a range of video applications indicate that there exists a slowly decaying autocorrelation structure in the underlying stochastic processes [3, 7, 8, 12, 19, 21].

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Besides periodic procedural testing, it is also important to test applications and how they perform in high-stress situations. Always remember that long service times and failures are indistinguishable to the average user. How does your system perform as load averages increase, especially when they do so over long periods of time What breaks first Fix those things, and move on to second- and third-level failures. Bad assumptions made in buffer or counter sizes will often be the first things exposed. Test the sort of problem that may not bite you for a while. The Y2K problem is an excellent example of this. Programs that were written in the 1970s that

Self-Similar Network Traf c and Performance Evaluation, Edited by Kihong Park and Walter Willinger ISBN 0-471-31974-0 Copyright # 2000 by John Wiley & Sons, Inc.

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Providing guarantees on maximum-delay, delay-jitter, and cell-loss probabilities (or some other measure of cell loss) in the presence of such traf c is nontrivial, especially if the coef cient of variation of the marginal distribution (or the distribution tail) is large. This is because such traf c signi cantly increases queue length statistics at a multiplexer [2, 5, 8, 16 18, 20]. As an illustration, consider the performance of a nite-buffer queue. Let the arrival process be a fractionally differenced autoregressive moving average process: 1 f1 B Dd Xt Et ; where f1 0 < f1 < 1 represents an exponentially decaying autocorrelation component; d 0 < d < 1 represents a hyperbolically decaying autocorrelation component; 2 fEt g is white noise, that is, uncorrelated; and B is the backward shift operator, that is, BXt Xt 1 . The correlation structure of Xt can be controlled by changing f1 and d. Figure 19.1 shows the mean cell loss versus number of frame buffers for three input correlation structures with approximately the same coef cient of variation $0:24 . A frame buffer is the maximum number of cells that can be transmitted by the output channel in a given time interval (frame time).

Fig. 19.1 Mean number of cells dropped for different dependency structures in the workload. (a) Mean utilization 0.9; (b) mean utilization 0.8. The model form used is a fractionally differenced ARIMA 1; d; 0 process [13]: 1 f1 B Dd Xt Et .

were believed to be temporary were the worst culprits for Y2K. Test unusual calendar alignments, like leap years (remember, the year 2100 is not a leap year!). Check for assumptions that may be broken in months with 6 weeks in them; if August 1st falls on a Friday or Saturday, then August stretches into the beginning of a 6th week on the 29th or 30th. Of course, it is difficult to test dates after 3:14 A.M. GMT on January 19, 2038, on Unix systems, as that is when their clock counters run out. (Yes, another Y2K-like crisis is approaching.)

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