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A simple application on Facebook can have a great impact or can fall at It can achieve great virality and impact sales or it can achieve great virality and have no impact on sales whatsoever In 2007, Johnson & Johnson created a Facebook application called the Acuvue Wink Like poking somebody or sending them a drink, this app let you choose the animated pair of winking eyes and the message to send to your friends This time, the branding worked When the campaign was at its peak, 10,000 Facebook users were winking every day Some 65,000 people downloaded the app between the beginning of August and the end of the year They were hoping to reach 100,000 people and got a half million individuals On the frequency scale, they were shooting for a half million winks and went over a million, or two winks per person.



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Figure 18.2 shows the estimated conditional probability densities for a 1:05, 1.95 traf c for time scales 500 ms, 1 s, and 5 s. In the following, T1 T2 .

of applications installed in clusters, rather than two copies. In most cases, upgrades are just not performed enough to justify the extra work associated with managing two separate sets of configuration files.

Fig. 18.2 Top row: Probability densities with L2 conditioned on L1 for a 1:05. Bottom row: Probability densities with L2 conditioned on L1 for a 1:95.





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The last, and most important, component of a service group is the critical application itself The good news is that most applications will just run in a clustered environment There are, of course, exceptions to this rule, but they are few and far between As long as applications write their data to disk on a regular basis and do not only preserve it in memory, they can usually function in a cluster without modification We speak specifically of applications that run on a single node at a time in a cluster There are more complex applications such as Oracle RAC that can run simultaneously in multiple hosts These types of applications are very complex and may introduce issues that can reduce performance or availability On the other hand, when properly implemented, they can offer significant increases in availability above the norm.

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For the aggregate throughput traces with a 1:05 Figure 18.2 (top row) the three-dimensional (3D) conditional probability densities can be seen to be skewed diagonally from the lower left side toward the upper right side. This indicates that if the current traf c level L1 is low, say, L1 1, chances are that L2 will be low as well. That is, the probability mass of PrfL2 jL1 1g is concentrated toward 1. Conversely, the plots show that PrfL2 jL1 8g is concentrated toward 8. This is more clearly seen in Fig. 18.3(a), which shows two cross sections, that is, 2D projections, re ecting PrfL2 jL1 1g and PrfL2 jL1 8g. For the aggregate throughput traces with a 1:95 (Fig. 18.2 (bottom-row)), on the other hand, the shape of the distribution does not change as the conditioning variable L1 is varied. This is more clearly seen in the projections of PrfL2 jL1 1g and PrfL2 jL1 8g shown in Fig. 18.3(b). This indicates that for a 1:95 traf c observing the past (over the time scales considered) does not help much in predicting the future beyond the information conveyed by the xed a priori distribution. Given the de nition of Lk , the Gaussian shape of the marginal densities is consistent with short-range correlations, making the central limit theorem approximately applicable over larger time scales. In both cases a 1:05, 1.95), the shape of the distribution stays relatively constant across a wide range of time scales 500 ms to 20 s. For a 1:35, 1.65 the predictability structure lies ``in-between'' (not shown here).

But parallel applications are the exception rather than the rule For most applications, the most pressing issue is whether or not a copy of the application is licensed to run on a particular server That is, in fact, a vital test that must be run during configuration of a cluster Every application must be run on every eligible system in the cluster, just to make sure that it really works there In some cases, enabling applications to run on every server will require getting a multitude of license keys from the application vendor, even if the application will only run on one node at a time Some vendors will discount failover copies of their software; others will not.

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