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When the variance s2 of the service time distribution is nite, the standard heavy traf c limit theorem for the stationary waiting time W in the M =G=1 queue holds, that is, lim P D r W

For more detailed information about clustering and failover, please see 15, Local Clustering and Failover. Estimated costs of implementation:

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Imitation may be the sincerest form of attery, but satire is still attery although it may be insincere and far from attering. Satire is a sign that you have become large enough to be a target. Measure it, yes; control it, no. There are many stories, starting with the Coke/Mentos video, of companies that tried to shut down an embarrassing online conversation only to discover that it can t be done. The discovery is usually in public and quite embarrassing. Instead, embrace those having fun at your expense and encourage them. Make a contest of it. In a blog post called When will the world make fun of you Seth Godin says, Spread the word, share the story. If it s worth telling, it s worth parodying. When will we be able to parody what you do (See http://sethgodin.typepad. com/seths blog/2009/10/when-will-the-world-make-fun-ofyou.html). When parodies are not in good fun. When online comments are not accurate, then it s time to call the lawyers.





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Second server: $42,000 Two licenses and media for clustering software: $12,100 Extra NICs and cables: $4,500

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5 years of vendor support for the clustering software: ($3,132 per year): $15,660 Clustering software training for two students: $6,000 Clustering software implementation services: $12,000 LVM and filesystem software for the second node: $6,000 5 years of LVM and filesystem software support for the second node ($1,380 per year): $6,900

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Rating 3.1

with D r : l 1 r = 1 l2 s2 =2 . This exponential heavy traf c theorem was obtained by Kingman in the early 1960s; see Kingman [25] for an early survey, and Whitt [36] for an extensive overview of heavy traf c limit theorems for queues. In Boxma and Cohen [10] (see also Cohen [19]) a heavy traf c limit theorem has been proved for the GI =G=1 FCFS queue in which the service time and=or interarrival time distribution is regularly varying, with in nite second moment. In Section 6.3.2 we discuss that result for the case of the M =G=1 FCFS queue; in Section 6.5 the case of the GI =G=1 FCFS queue will be treated. The known heavy traf c result for the M =G=1 PS case is presented in Section 6.3.3. Section 6.3.4 contains a new heavy traf c limit theorem for the waiting time distribution in the M =G=1 LCFS-PR queue with a heavy-tailed regularly varying service time distribution.

The total cost of implementation is $105,160 Note that these costs are for the first cluster Additional clusters will cost less because implementation costs will drop Once their system administrators have been trained, there will not be any need to send them to training every time they implement a cluster Plus, after a few installs, it will probably not be necessary to employ implementation services for every cluster Per-server costs also decline as cluster sizes increase Because we are considering implementing clustering software, we must examine all the outages that it will affect Table 32 lists the outages we will consider, along with their durations and likelihoods before the clustering software is implemented Given all the statistics in Table 32, we get: RB = $75 9,360 = $702,000 The 99644 percent availability in the last box in Table 3.

In Boxma and Cohen [10] the following result has been proved: Theorem 6.3.1. For the stable M =G=1 FCFS queue with regularly varying service time distribution of index n, as speci ed in Eq. (6.2), the ``contracted'' waiting time DW r W =b converges in distribution for r 4 1. The limiting distribution Rn 1 t is speci ed by its LST 1= 1 rn 1 , Re r ! 0, and the coef cient of contraction DW r is the root of the equation r G 2 n n 1 x L x 1 r; n 1 x > 0; 0 < 1 r ( 1; 6:19

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