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ultimate realization as self-similar link traf c. Ef ciency dictates that le transmissions, including retransmission of lost packets, complete in a short amount of time. Subject to the limitations of congestion control in achieving optimally ef cient transfer [21], this has the effect of stretching out a large le or message transfer in time into an on-average, ``thin'' packet train. This also suggests why the linear on=off model may have been successful in modeling the output characteristics of a complicated nonlinear system, which real networks undoubtedly are. In some sense, the effect of the unaccounted-for nonlinearity is re ected back as a stretching-intime effect, thus conforming to the model's original suppositions. 14.4.7 Network Performance



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In this section, we present a summary of performance results evaluating the effects of self-similarity. A comprehensive study of the performance implications of selfsimilarity including quality-of-service (QoS) issues, resource trade-offs, and performance comparisons between TCP Reno, Tahoe, and Vegas can be found in Park et al. [20]. 14.4.7.1 Performance Evaluation Under Reliable Communication We evaluated network performance when both traf c self-similarity (a of Pareto le size distribution) and network resources (bottleneck bandwidth and buffer capacity) were varied. Figure 14.11(a) shows packet loss rate as a function of a for buffer sizes in the range 2 128 kB. We observe a gradual increase in the packet loss rate as a approaches 1, the atness of the curve increasing as buffer capacity is decreased. The latter is due to an overextension of buffer capacity whereby the burstiness associated with a 1:95 traf c is already high enough to cause signi cant packet drops. The added burstiness associated with highly self-similar traf c (a 1:05) bears little effect. The same gradual behavior is also observed for packet retransmission and throughput. Figure 14.11(b) shows mean queue length as a function of a for the same buffer range. In contrast to packet loss rate, queueing delay exhibits a superlinear dependence on self-similarity when buffer capacity is large. This is consistent

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Remember that one of the constraints on availability is MTTR. Automate your client provisioning, and keep a good supply of spare desktops on hand to reduce your MTTR and eliminate user-introduced variations that lead to future problems.

(b)

Thin clients are one extreme endpoint in client reliability. Thin refers to the lack of local disk, local operating system, or user-visible processing; most thin clients are either X terminals or extended terminal devices like Sun s SunRay client. Thin clients improve client-side reliability by taking away opportunities for bad configuration, viruses, undocumented changes, and local disks to bring down clients. Client-side availability becomes a function of physical device availability and network connectivity. Reducing the number of moving parts a user can interact with helps reliability because it removes potential failure entry points. The overall user perception, however, is dependent on the client device as well as the servers that run user applications and drive the interfaces to these thin clients. Part of the problem shifts from client-side software to server-side applications. Moving your entire office to a thin client model but hanging all of those clients off of a nonclustered single large server ensures that when you have your first system reboot, all of your users are going to be angry. Thin clients work well in conjunction with the application conditioning and server clustering patterns we cover in the next several chapters. Once you ve gotten a grip on the hardware side of the client equation, it s time to look at how client applications behave or misbehave when the data services they re consuming fail.

Some companies are distributed and some are hierarchical. All Internet projects started in a distributed manner because the individual who rst created a web site, rst posted a blog, or rst tweeted did so without a net. They were mavericks acting on their own. Later, they discovered others doing the same thing and they were thrilled to nd allies in the cause. But individual efforts are not the most ef cient. Suresh Vittal, principal analyst at Forrester Research, promotes the idea of shared learning. With any big organization, Suresh says, support groups are looking at this problem and saying, Well, if the toothpaste guy goes off and spends a million dollars and the shampoo guy goes off and spends a million dollars and the soap guy spends a million dollars, then we re going to create a central service team because the interaction affects their best practices. The service organization should not hinder the creative efforts but support their execution.

Fig. 14.11 TCP run. (a) Packet loss rate and (b) mean queue length as a function of a; (c) queueing delay-packet loss trade-off curve.

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