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We use a generic buffer model [13] to explore the effects of long-range dependence. The buffer has capacity c and receives inputs (e.g., frames consisting of cells) at equally spaced deterministic times T1 ; T2 ; . . . : This assumption is appropriate for video sources because video frames are meant to be transmitted at equally spaced times. Let Xi be the number of arrivals (packets or cells) at time Ti ; i 1; 2; . . . : Let di be the number of items that are processed during Ti ; Ti 1 , which is called the ith interval. The length of an interval is the time to transmit one frame of video (40 ms for PAL video and 33.33 ms for NTSC video). This model applies to other types of traf c when packet or byte counts over a xed time interval are used to describe the traf c. This is what Makowski and Parulekar do in 9 in this volume. Let Vi be the buffer content at the end of the ith interval. We have the following law of motion for Vi ; i 1; 2; . . . : V0 is given, and Vi minf Vi 1 Xi di ; cg; i 1; 2; . . . ; 12:8



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To be fair, there are some standard benchmark suites that have been run reliably across different platforms and applications for a long time Although these benchmarks certainly allow for a fairer comparison between different hardware and software combinations, unless the conditions under which they were run match your environment precisely, they are still not especially valuable When a comparative benchmark is taken, and only one variable has been changed, the result is an interesting study that shows the benefits of the new variable But, again, whether that variable will work as well in your own environment cannot be determined without running similar benchmarks in that environment The only way to be sure that a product s performance will be adequate for your computing environment is to run your own benchmark in an environment that is as close to the production environment in which the deployed products will run as possible.





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where x max x; 0 . We will take V0 0 to simplify the exposition. Thus, Vi is the smaller of the two numbers the buffer capacity and the buffer content at the start of the ith interval, plus what arrived at the buffer, minus what went out. We take di as a constant d, which is the number of packets or cells that can be processed in these intervals; for example, if 10,000 cells can be processed per second

That means that the test environment must include the same system hardware, the same storage hardware, the same networking and interconnects, the same type and size of data, the same applications, the same user loads, the same system loads, the same number of active users on the system, and so much more What s more, all software and firmware must be at the same patch levels Despite all of this, if you run the same benchmark 10 different times, you can easily get 10 different sets of results (continued).

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and the length of a frame interval is 40 ms, then d 10;000=25 400. The arrivals are assumed to ow in such a way that the output is min Vi 1 Xi ; d . This will occur when the arrivals are spread uniformly over the frame interval (as suggested by Ott et al. [25]) and the cells are processed at a constant rate. When the Xi are stationary with common mean l, say, the traf c intensity is l=d. 12.4.1.1 Solution of the Model with an In nite Buffer The model is easily solved when c I, so we do that rst. In this case, Eq. (12.8) becomes (recall that di d Vi Vi 1 Xi d ; i 1; 2; . . . ; 12:9

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which we recognize as Lindley's equation for the delay of the ith customer in a GI =G=1 queue, where the Xi 's are interarrival times and the d's are service times. De ne the partial sums S0 0 and Sm Let Yi Xi d; note that

OFF TO THE RACES: SOME THOUGHTS ON BENCHMARKS (continued)

Xi ;

Sound difficult You bet it is. But it s the only way for your benchmarks to have any relevance to your environment. As soon as you change even one variable, the benchmark loses most, if not all, of its relevance. And if the benchmark isn t relevant, why take the time and effort to run it

m > 0:

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