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The VBR video sequences we have examined all have large autocorrelations at short lags as well as slowly decaying autocorrelations at long lags. For video conferences, the autocorrelation function is a geometric function for small lags, and source models based on this property when used in a performance model yield similar results to simulations using traces as the source. Entertainment video is more complex than video conferences because of the effects of scene changes. We nd that the same class of models used for video conferences apply to scenes of entertainment video, and introducing the effects of scene changes and accounting for the random lengths of scenes we may obtain a usable source model. Such a model may not perform as well as an empirical Markov chain model. We explain the success of these source models that allow only short-range dependence by deriving the resetting effect and the trancating effect of nite buffers from an analysis of a generic buffer model. These effects are shown to have a strong in uence on the performance analysis of VBR video. We have restricted our attention to VBR video coded according to the H.261 standard. The video community is currently exploring coding with MPEG standards. These codings generate a periodic structure to the frame sizes, so they are not directly amenable to modeling with stationary processes. The models we use here may provide building blocks for MPEG-encoded VBR video. Chandra and Reibman [4] and Heyman et al. [17] are rst steps in this direction.



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Intuit is a clued-in organization. The number and caliber of people they have addressing these issues is impressive. They are not shy about using the best tools on the market. They like exploring new possibilities. But Seth is concerned about one area. I think we re pretty good in terms of measuring stuff, he says. But sometimes that hinders us. Sometimes it sti es some of the creativity in how far we want to strive for. For example, Citizen Kane, which was directed by Orson Welles, is a masterpiece because he didn t know what the heck he was doing. He kept pushing for things from the cinematographer you were not supposed to ask for. When we start understanding how to measure stuff, we start putting limitations on the creative side. Instead of trying to reach for the stars and then gure out how to measure it. That s why we ll always need 20-year-olds. They don t know what can t be done.





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You wouldn t take a stranger on a tour of the vault where the company stores its cash. ( And over here are our $100 bills. ) Why take them into the data center If they don t need to be there to do their job, don t let them in.

1. J. Beran, R. Sherman, M. Taqqu, and W. Willinger. Long-range dependence in variable-bitrate video traf c. IEEE Trans. Commun., 43:1566 1579, 1995. 2. P. Billingsley. Statistical Inference for Markov Processes. University of Chicago Press, Chicago, 1961. 3. P. Bratley, B. L. Fox, and L. E. Schrage. A Guide to Simulation. Springer-Verlag, Berlin, 1987. 4. K. Chandra and A. R. Reibman. Modeling two-layer MPEG-2 video traf c. In H. L. Bertoni, Y. Yang, S. P. Wang, and S. Panwar, eds., Multimedia Communications and Video Coding. Plenum Press, New York, 1996. 5. C. Courcoubetis and R. Weber. Buffer over ow asymptotics for a buffer handling many traf c sources. J. Appl. Probab., 33:886 903, 1996.

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On yet another data center tour, a system administrator was hurriedly bringing some equipment down the row that we were standing in. Everyone on the tour stepped aside as best they could to let him pass. In my haste to get out of the way, I backed into an old-fashioned line printer, and right into the power switch, turning it off. The printer was printing something, so the job was lost. I saw what I had done and quickly (and very smoothly) turned it back on. I don t think anyone noticed (except for the person who had printed the job). Even well-intentioned and careful visitors to data centers can cause problems. Evan

6 D R Cox and D Oakes Analysis of Survival Data Chapman and Hall, New York, 1984 7 A Elwalid, D Heyman, T V Lakshman, D Mitra, and A Weiss Fundamental bounds and approximations for ATM multiplexing with applications to video teleconferencing IEEE J Select Areas Commun, 13(6):1004 1016, 1995 8 A I Elwalid and D Mitra Effective bandwidth of general Markovian traf c sources and admission control of high speed networks IEEE=ACM Trans Networking, 1(3):329 343, 1993 9 M R Frater, J F Arnold, and P Tan A new statistical model for traf c generated by VBR coders for television on the broadband ISDN IEEE Trans Circuits Syst Video Technol, 4(6):521 526, December 1994 10 M W Garrett and M Vetterli Congestion control strategies for packet video In Fourth International Workshop on Packet Video, Kyoto, Japan, August 1991 11 D P Heyman.

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