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Bad documentation, inadequate testing, and poorly educated users and administrators can be just as damaging to system availability as technical failures. Treat your vendors like people, and in turn they will take better care of you. Realize that your system administration staff is like engine oil. It s only a commodity when it s on the shelf. When they are working for you, it s your responsibility to take care of them. Security, a topic worthy of many books all by itself, is a critical part of system availability.



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idealized line for a close to 1, and above the line for a close to 2. Figure 14.3(b) shows similar results for the case in which there is no signi cant limitation in bandwidth (155 Mb=s) leading to zero packet loss. There is noticeably more spread among the estimates, which we believe to be the result of more variability in the traf c patterns since traf c is less constrained by bandwidth limitations. Figure 14.3(c) shows the results when bandwidth is limited, as in the baseline case, but buffer sizes at the switch are increased (64 kB). Again, a roughly linear relationship between the heavy-tailedness of le size distribution (a) and self-similarity of link traf c (H) is observed. To verify that this relationship is not due to speci c characteristics of the TCP Reno protocol, we repeated our baseline simulations using TCP Tahoe and TCP Vegas. The results, shown in Figure 14.4, were essentially the same as in the TCP Reno baseline case, which indicates that speci c differences in implementation of





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We have built a base of redundancy constructs centered around your data, its management, and the environment in which everything is housed. An end-toend availability design needs to consider the consumers of the services, as well as the network that delivers the well-behaved services from the data center to the client machines. In this chapter, we examine the issues surrounding client availability, ranging from having clients for users to various impacts of service failure on client applications. We re at Level 6 in the Availability Index, as shown in Figure 12.1. Clients sit squarely between the data center environment and the application layer, because they represent the user interface to any availability hardening. No clients, no availability. What should available services expect of their clients To paraphrase our founding father, Not much. There may be cases where a complex Web-based application needs to persist and restore in-flight state information, but we ll cover that as a server-side problem, not a client issue. This is purely about the client side of the problem that is, we re assuming the applications that are running on the clients aren t highly available themselves, but need to preserve the best possible user experience in the face of failures. In 13, we ll go up another level in the Index to explore application design consideration for making them more fault-resilient.

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Fig. 14.4 Hurst parameter estimates for (a) TCP Tahoe and (b) TCP Vegas runs with a 1.05, 1.35, 1.65, 1.95.

TCP's ow control between Reno, Tahoe, and Vegas do not signi cantly affect the resulting traf c self-similarity. Figure 14.5 shows the relative le size distribution of client=server interactions over the 10,000 second simulation time interval, organized into le size buckets (or bins). Each le transfer request is weighted by its size in bytes before normalizing to yield the relative frequency. Figure 14.5(a) shows that the Pareto distribution with a 1:05 generates le size requests that are dominated by le sizes above 64 kB. On the other hand, the le sizes for Pareto with a 1:95 (Fig. 14.5(b)) and the exponential distribution (Fig. 14.5(c)) are concentrated on le sizes below 64 kB, and in spite of ne differences, their aggregated behavior (cf. Figure 14.2) is similar with respect to self-similarity. We note that for the exponential distribution and the Pareto distribution with a 1:95, the shape of the relative frequency graph for the weighted case is analogous to the nonweighted (i.e., one that purely re ects the frequency of le size requests) case. However, in the case of Pareto with a 1:05, the shapes are ``reversed'' in the sense that the total number of requests are concentrated on small le sizes even though the few large le transfers end up dominating the 10,000 second simulation run. This is shown in Figure 14.6.

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