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It is useful to distinguish three kinds of quality of service measures, which we refer to here as transparency, accessibility, and throughput. Transparency refers to the time and semantic integrity of transferred data. For real-time traf c delay should be negligible while a certain degree of data loss is tolerable. For data transfer, semantic integrity is generally required but (per packet) delay is not important. Accessibility refers to the probability of admission refusal and the delay for setup in case of blocking. Blocking probability is the key parameter used in dimensioning the telephone network. In the Internet, there is currently no admission control and all new requests are accommodated by reducing the amount of bandwidth allocated to ongoing transfers. Accessibility becomes an issue, however, if it is considered necessary that transfers should be realized with a minimum acceptable throughput. Realized throughput, for the transfer of documents such as les or Web pages, constitutes the main quality of service measure for data networks. A throughput of 100 kbit=s would ensure the transfer of most Web pages quasi-instantaneously (less than 1 second). To meet transparency requirements the network must implement an appropriately designed service model. The accessibility requirements must then be satis ed by network sizing, taking into account the random nature of user demand. Realized throughput is determined both by how much capacity is provided and how the service model shares this capacity between different ows. With respect to the above requirements, it proves useful to distinguish two broad classes of traf c, which we term stream and elastic. 16.2.2 Stream Traf c

III 50 II I 0 Dose (a)

Stream traf c entities are ows having an intrinsic duration and rate (which is generally variable) whose time integrity must be (more or less) preserved by the network. Such traf c is generated by applications like the telephone and interactive video services, such as videoconferencing, where signi cant delay would constitute an unacceptable degradation. A network service providing time integrity for video signals would also be useful for the transfer of prerecorded video sequences and, although negligible network delay is not generally a requirement here, we consider this kind of application to be also a generator of stream traf c. The way the rate of stream ows varies is important for the design of traf c controls. Speech signals are typically of on=off type with talkspurts interspersed by silences. Video signals generally exhibit more complex rate variations at multiple

Response (Probit)

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Virology 1985, 143(1), 290 299 73 Marcovistz, R; Germano, P M; Riviere,Y; Tsiang, H; Hovanessian, A G The effect of interferon treatment in rabies prophylaxis in immunocompetent, immunosuppressed, and immunode cient mice J Interferon Res 1987, 7(1), 17 27 74 Witt, P L; Marie, I; Robert, N; Irizarry, A; Borden, E C; Hovanessian, A G Isoforms p69 and p100 of 20 ,50 -oligoadenylate synthetase induced differentially by interferons in vivo and in vitro J Interferon Res 1993, 13, 17 23 75 Schroder, H C; Wenger, R; Kuchino, Y; Muller, W E G Modulation of nuclear matrixassociated 20 ,50 -oligoadenylate metabolism and ribonuclease L activity in H9 cells by human immunode ciency virus J Biol Chem 1989, 264, 5669 5673..

time scales. Importantly for traf c engineering, the bit rate of long video sequences exhibits long-range dependence [12], a plausible explanation for this phenomenon being that the duration of scenes in the sequence has a heavy-tailed probability distribution [10]. The number of stream ows in progress on some link, say, is a random process varying as communications begin and end. The arrival intensity generally varies according to the time of day. In a multiservice network it may be natural to extend current practice for the telephone network by identifying a busy period (e.g., the one hour period with the greatest traf c demand) and modeling arrivals in that period as a stationary stochastic process (e.g., a Poisson process). Traf c demand may then be expressed as the expected combined rate of all active ows: the product of the arrival rate, the mean duration, and the mean rate of one ow. The duration of telephone calls is known to have a heavy-tailed distribution [4] and this is likely to be true of other stream ows, suggesting that the number of ows in progress and their combined rate are self-similar processes. 16.2.3 Elastic Traf c

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