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The same problem exists when using simulation as the principal performance evaluation tool. Traf c Control Traf c control for self-similar traf c has been explored on two fronts: (1) as an extension of performance analysis in the resource provisioning context, and (2) from the multiple time scale traf c control perspective where correlation structure at large time scales is actively exploited to improve network performance. The resource provisioning aspect advocates a small buffer=large bandwidth resource dimensioning policy, which when coupled with the central limit theorem yields predictable multiplexing gains when a large number of independent ows are aggregated. Whereas resource provisioning is open-loop in nature, multiple time scale traf c control seeks to achieve performance gains by exploiting correlation structure in self-similar traf c at time scales exceeding the time horizon of the feedback loop to impart proactivity to reactive controls (e.g., TCP). This is relevant in broadband wide area networks where the delay bandwidth product problem is especially severe, and mitigating the performance degradation due to outdated feedback is critical to facilitating scalable, adaptive traf c control. The initial success of this approach [62, 67 69] (see 18 for an application to rate-based congestion control) leads to a generalization to workload-sensitive traf c control, where facilitation of workload sensitivity is expanded along several traf c control dimensions including the two core features for harnessing predictability at large time scales: long-range correlation in network traf c and heavy-tailedness of connection durations. Workload-sensitive traf c control is a broad area that can bene t from concerted efforts at several fronts, spanning novel mechanisms for detecting and exploiting large time scale predictability structure, short-duration connection management, packet scheduling, end system support, and dynamic admission control with self-similar call arrivals and=or heavy-tailed connection durations.

Time striping divides a video stream into xed-length (in time) stripe units. Another approach is to divide a video stream into xed-size (in bytes) stripe units space striping. Space striping simpli es storage and buffer management in the servers because all stripe units are of the same size. Moreover, the amount of data sent by each server in a service round is also the same. Unlike time striping, we do not need to know the frame structure in order to perform striping, thus decoupling the striping algorithm from the encoding format. This space striping approach has been employed in many studies [10, 12 18]. Depending on the system design, the stripe unit size can range from tens of kilobytes to hundreds of kilobytes. In most of the studies, a stripe unit is assumed to play back in a constant length of time. However, in most video compression algorithms such as MPEG, a xed-size stripe unit will likely contain a variable number of frames and/or partial frames. Moreover, if the video is compressed using constant-quality compression algorithms (cf. Section 2.4), then the video bit-rate will become variable as well. Consequently, the decoding time for a stripe unit at the

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21.2 21.2.1

method, in which rabbit anti-mouse IgG antibody is bound to the mouse monoclonal, and an anti-rabbit IgG antibody, with its two binding sites, is sandwiched between the rabbit anti-mouse IgG and a 2:1 peroxidase: rabbit antiperoxidase complex Finally, Lane C shows detection using a biotinylated anti-mouse IgG antibody followed by a streptavidin peroxidase conjugate, which is stained for activity Lanes A and C clearly indicate better immunoblot selectivity, and this results, in part, from limited use of polyclonal antisera in the immunological reactions A nal example of immunoelectrophoresis methods is the quantitative method called rocket electrophoresis, that employs secondary antibody antigen interactions for detection16 The rocket method uses thin agarose gels, typically 1 2%, that contain $1 % antibody; the antigen is a protein (ie, univalent and multideterminate) and the antibody is a polyclonal antiserum.

client will be variable and this may cause playback starvation unless suf cient buffering is done at the client before playback begins.

Unlike many systems of study including economic, social, and certain physical sciences (e.g., astronomy, earth and atmospheric science), network systems admit to design, implementation, and controlled experimentation of the underlying physical system at nontrivial scales for example, protocol deployment in autonomous systems belonging to a single service provider which facilitates an intimate, mechanistic understanding of the system at hand. Model selection is not bound by ``black box'' evaluations, and physical models that can explicate traf c characteristics in terms of elementary, veri able system properties and network mechanics, in addition to data tting, provide an opportunity to be exploited. The challenge lies in combining relevant features from workload modeling, network architecture protocols and transmission technology user behavior, and analytical modeling into a

consistent, effective description of network systems, in particular, the Internet. As such, physical modeling is a research program that transcends workload modeling, encompassing both performance analysis and traf c control. 21.2.2 Multifractal Traf c Characterization

In the previous discussions, we have assumed a round-robin placement of the stripe units across the servers in the system. If we denote the stripe units of a video stream using v0 , v1 , . . . , etc., then stripe unit vi will be stored in server (i mod N S ). However, a minor problem with this policy is that server i will likely store more stripe units than server j, for i < j. This is because the length of a video is not always an integral multiple of the size of a stripe. Therefore, the last stripe will likely contain less than N S stripe units, lling from server zero. To balance the storage, we can modify the round-robin policy to start striping a new video stream from different servers. Apart from round-robin placement, Tewari et al. [17] also investigated a random-placement policy where the order within a stripe is permuted pseudo-randomly. They pointed out that the round-robin placement policy can introduce a convoy effect when one server becomes overloaded. That is, the overloading condition will shift from one server to the next due to the round-robin placement. By permuting the order of the stripe units in each stripe, this convoy effect can be avoided.

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