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Brand is grand. Awareness is compareless. Intent is heaven sent. But what about sales Intuit is now ready to build out the sales funnel for the next go-round. The funnel starts off with total awareness, unaided and aided, familiarity with the brand, and the brand image attributes above. Purchase intent is next followed by actual purchase. They asked the basic research question How did you hear about us Next time, given experience and foresight, they are prepared to do a pre- and a post-analysis to get an even more rigorous control group.



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We have become obsessed with data. If we can measure it, in theory, we can control it and improve it. Data collected and analyzed becomes the basis for resource allocations and prioritization decisions, ranging from whether or not to buy an additional 10 terabytes (TB) of disk space for marketing, to which server pairs should be turned into clusters for improved availability. Our goal for this chapter is to present some common measurements of availability, and to provide a framework for interpreting those measurements. In 3, The Value of Availability, we ll ascribe pricing and business benefit to this data. In this chapter we discuss the following topics:





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an M =G=I queue and generate sample paths with autocorrelation function given by Eq. (12.20). Suppose Eq. (12.20) is the autocorrelation function of the ``true'' fXn g in Eq. (12.18). The SRD version of the true process has a purely geometric autocorrelation function kx I , and the LRD version has a purely hyperbolic autocorrelation function kx 1 . We chose parameters based on video conference C: l 130; a 0:985; H 0:7; and kx 250:

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We take service rates that vary from 135 to 160 in steps of 5, so the traf c intensities range from 0.813 to 0.963 (cell losses are zero for smaller traf c intensities). Buffer sizes vary from 0 to 1 second. Each simulation was run for 500,000 iterations of Eq. (12.8). Figure 12.17 shows cell-loss rates averaged over 30 sample paths for each of the three input processes. The cell-loss rate increases with the traf c intensity, so the topmost LRD curve should be compared to the topmost source curve, and so on. It is clear that the SRD version is a very good representation of the true process for cellloss calculations, and that the LRD version is a very poor representation. This could be predicted from the analytical results. The points to be learned from this example are that large correlations at short lags can be the dominant feature of the autocorrelation function, and that the presence of long-range dependence may not be relevant in a performance study. Note that an extreme case of the truncacting effect of nite buffers appears in Fig. 12.17; all three inputs yield the same loss rate when there is no buffer.

How we measure availability The failure modes, or typical things that can and do go wrong Situations in which measurements may not be valid

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Throughout this book we use resiliency in terms of overall system availability. We see resiliency as a general term similar to high availability, but without all the baggage that HA carries along with it. High availability once referred to a fairly specific range of system configurations, often involving two computers that monitor and protect each other. During the last few years, however, it has

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lost much of its original meaning; vendors and users have co-opted the term to mean whatever they want it to mean. To us, resiliency and high availability mean that all of a system s failure modes are known and well defined, including networks and applications. They mean that the recovery times for all known failures have an upper bound; we know how long a particular failure will have the system down. Although there may be certain failures that we cannot cope with very well, we know what they are and how to recover from them, and we have backup plans to use if our recoveries don t work. A resilient system is one that can take a hit to a critical component and recover and come back for more in a known, bounded, and generally acceptable period of time.

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