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some other form of giveback There are so many factors that are out of the control of system vendors, and are therefore disallowed in the contracts, that those contracts seldom have any teeth, and even more seldom pay off Compare, for instance, the potential reliability of a server located in a northern California data center where, in early 2001, rolling power blackouts were a way of life, with a server in, say, Minnesota, where the traditionally high amounts of winter snow are expected and do not traditionally impact electric utility service Despite those geographical differences, system vendors offer the same uptime contractual guarantees in both places A system vendor cannot reasonably be expected to guarantee the performance of a local electric power utility, wide area network provider, or the data center cooling equipment Usually, those external factors are specifically excluded from any guarantees.



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(Weighted) file size distribution: Pareto 1.05 (Weighted) file size distribution: Pareto 1.95 (Weighted) file size distribution: exponential





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Dec 2, 2016 · I have lost the IRIS OCR software on my laptop. Were can I go to download it ? - 5382507.

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The other problem with the nines is that availability is a chain, and any failed link in the chain will cause the whole chain to fail Consider the diagram in Figure 21, which shows a simple representation of a user sitting at a client station and connected to a network over which he is working If the seven components in the figure (client station, network, file server and its storage, and the application server, its application, and its storage) have 9999 percent availability each, that does not translate to an end user seeing 9999 percent availability To keep the math simple, let s assume that all seven components have exactly the same level of expected availability, 9999 percent In reality, of course, different components have different levels of expected availability, and more complex components such as networks will often have lower levels.

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The other assumption is that multiple failures do not occur at the same time (although they can, of course, in real life); that would needlessly complicate the math..

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So create your multi-layered PowerPoint stack that will tell the whole story. But then gure out to whom you are pitching so you ll know which part of the story they need.

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Availability of 99.99 percent over each of seven components yields a simple formula of 0.9999 to the seventh power, which works out to 99.93 percent. That may not sound like a huge difference, but the difference is actually quite significant:

Fig. 14.5 Relative frequency of weighted le size distributions obtained from three 10,000 second TCP runs Pareto (a) with a 1:05 and (b) with a 1:95; (c) exponential distribution.

Fig. 14.6 Relative frequency of unweighted le size distributions of TCP runs with Pareto (a) with a 1.05 and (b) with a 1.95; (c) exponential distribution.

Availability of 99.99 percent spread over a year is about 52 minutes downtime. Availability of 99.93 percent spread over a year is over 6 hours of downtime.

All the runs thus far were obtained with an exponential idle time distribution with mean 600 ms. Figure 14.7(a) and (b) show the H-estimates of the baseline con guration when the idle time distribution is exponential with mean 0.6 s and Pareto with a 1:05 and mean 1.197 s. The le size distribution remained Pareto. As the H-estimates show, the effect of a Pareto-modeled heavy-tailed idle time distribution is to boost long-range dependence when a is close to 2, decreasing in effect as a approaches 1. This phenomenon may be explained as follows. For le size distributions with a close to 2, the correlation structure introduced by heavy-tailed idle time is signi cant relative to the contribution of the le size distribution, thus increasing the degree of self-similarity as re ected by H. As a approaches 1, however, the tail mass of the le size distribution becomes the dominating term, and the contribution of idle time with respect to increasing dependency becomes insigni cant in comparison. Figure 14.7(c) shows the Hurst parameter estimates when the le size distribution was exponential with mean 4.1 kB, but the idle time distribution was Pareto with a ranging between 1.05 and 1.95 and mean 1.197 s at a 1:05. As the idle time distribution is made more heavy-tailed a 3 1 , a positive trend in the H-estimates is discernible. However, the overall level of H-values is signi cantly reduced from the case when the le size distribution was Pareto, indicating that the le size distribution is the dominating factor in determining the self-similar characteristics of network traf c. 14.4.4 Effect of Traf c Mixing

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