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3) implies (and is implied by) asymptotic (second-order) self-similarity; that is, after appropriate rescaling, the overall traf c rate processes X m have identical second-order statistical characteristics and ``look similar'' for all suf ciently large.

Grierson, in Gel Electrophoresis of Nucleic Acids: A Practical Approach, D Rickwood and B D Hames, Eds, Oxford University Press, New York, 1982, pp 29 31 18 P G Righetti, Immobilized pH Gradients: Theory and Methodology, Elsevier, New York, 1990, pp 158 160 19 G H Keller and M M Manak, DNA Probes, Macmillan, New York, 1989, pp 1 23 20 M Nei and W-H Li, Proc Natl Acad Sci USA 76, 1979, 5269 21 G H Keller and M M Manak, DNA probes, Macmillan, New York, 1989, pp 76 79..

time scales m. In other words, Eq. (20.3) holds if and only if for all suf ciently large time scales m1 and m2, we have m1 H X m1 % m1 H X m2 ; 1 2 20:4

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Here, we assume that the total size of server buffer in between the media server application and the network (eg, the buffer inside the socket library and TCP) is a known constant, denoted by Z At the receiver, many existing video players will prefetch a certain amount of video data before starting playback to absorb the inevitable bandwidth uctuations We denote the playback duration of the prefetched video data by B p seconds Depending on the speci c player software, B p can be a xed value known to the server, or it can be con gurable by the users If it is the latter case and the existing player software does not report this value to the server, the server will simply assume the worst case of no prefetch, ie, B p = 0 second, in performing rate adaptation.

where the quality is in the sense of second-order statistical properties and where 1 2 < H < 1 denotes the self-similarity parameter and agrees with the Hurst parameter in Eq (203) The ability to explain the empirically observed self-similar nature of aggregate data traf c in terms of the statistical properties of the individual connections that make up the overall traf c rate process shows that (asymptotically) self-similar behavior (1) is an intrinsically additive property (ie, aggregate over many connections), (2) is mainly caused by user=session=connection characteristics (ie, Poisson arrivals of sessions, heavy-tailed distributions with in nite variance for the session sizes=durations), and (3) has little to do with the network (ie, the predominant protocols and end-to-end congestion control mechanisms that determine the actual ow of packets in modern data networks).

PROBLEMS 1. A mixture of cytochrome c and myoglobin are to be separated by polyacrylamide gel electrophoresis. Their isoelectric pH values (pI) are 9.6 and 7.2, and their molecular weights are 11.7 and 17.2 kDa, respectively. In which direction will each protein migrate (toward the anode or the cathode) at (a) pH 6.0 and (b) pH 8.5 2. Two monomeric proteins, X and Y, of MW 16.5 and 35.4 kDa, respectively, were treated with SDS in order to separate them by SDS PAGE.

In the case of client buffer starvation during video playback, it is assumed that playback will be paused until B p seconds worth of video data is again buffered at the client as depicted in Figure 82 Let G be the video frame rate After the late arrival of frame i, the playback is paused and the client will have to rebuffer frames i to (i + B p G 1) before resuming playback This rebuffering mechanism is common among commercial video player software such as Microsoft s Media Player and RealNetworks Video Player..

In fact, for the self-similarity property of data traf c over large time scales to hold, all that is needed is that the number of packets or bytes per connection is heavy tailed with in nite variance, and the precise nature of how the individual packets within a session or connection are sent over the network is largely irrelevant.

Note that this understanding of data traf c started with an extensive analysis of measured aggregate traf c traces, followed by the statistically well-grounded conclusion of their self-similar or fractal characteristics, and triggered the curiosity of networking researchers who wanted to know: ``Why self-similar or fractal '' In turn, this question for a physical explanation of the large-time scaling behavior of measured data traf c resulted in ndings about data traf c at the connection level that are, at the same time, mathematically rigorous, agree with the networking researchers' experience, are consistent with data, and are intuitive and simple to explain in the networking context.

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