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mainframes As a result, power conditioning, fire protection, and environmental controls are still valuable and practical with today s systems Modern fire protection systems are safer and cleaner than Halon, but in the data center they can still be centralized into a relatively small space Power requirements run the gamut, and 220-volt lines are still common requirements for larger systems Data centers offer significant advantages and disadvantages as compared to leaving critical systems in regular work areas The following are some advantages: Centralized resources A data center is the perfect place to coordinate all the specialized requirements of large, critical machines: conditioned 220-volt power, fire protection, security, battery backups, raised floors, atmospheric control, and so on If systems were deployed at random throughout the facility, those resources would need to be available everywhere By limiting their deployment to the data center, a tremendous cost saving can be achieved Physical security.



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c 8 <0 V2 Y1 Y2 : c if Y2 < Y1 ; if Y1 Y2 c Y1 ; if Y2 > c Y1 : 12:12





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Data centers work well to prevent inappropriate physical access to systems But since they are centralized and they contain lots of large, impressive-looking equipment, there is a tendency to show them off through data center tours (see Data Center Tours, later in this chapter), which limit security But there are disadvantages too: Limited possibilities for expansion If your systems need to expand beyond the capacity of your rack, it may not be possible to get another rack adjacent to the first one This can lead to complicated and messy layouts of systems, as they spread themselves in a seemingly random fashion all over the data center, like fast-food toys in a family minivan In that event, there may be issues with cable lengths, especially when SCSI is involved, that make such expansion impossible Putting all your eggs in one basket Centralizing your systems can be a double-edged sword.

Case 3: c < Y1 8 <0 V2 C Y2 : c if Y2 < c; if c Y2 if Y2 > 0: 0; 12:13

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Countering all the advantages of centralization, that we discussed previously, is the prospect that a single calamity could strike the data center and wipe out most or all of the company s computing resources at the same time Of course, this can be addressed by maintaining multiple data centers at separate facilities, a topic we cover at length in 20, The Disaster Recovery Plan Some people prefer to keep their clustered systems at opposite ends of their data center, to limit the impact of a localized external calamity such as a ceiling or floor collapse, or an overhead pipe leak Too much physical security Some security is critical, but too much security will do little more than impede the ability of system administrators to get their jobs done In our experience, good security doesn t keep the.

In Case 1, the rst busy period has length one, and we see the resetting effect as before In Case 3, we see that when there is a buffer over ow at time 1, the value of Y1 does not affect V2 The effect of Y1 on V2 is in the fact that Y1 > c; how much larger than c it is, is irrelevant This is an enhancement to the resetting effect, which we call the truncating effect of nite buffers Suppose that a particular sequence fXi g and parameters c and d d0 produce the cell-loss rate z0 , say It is intuitively obvious, and deducible from Eq (128), that increasing d to d1 > d0 yields a cell-loss rate, say, z1 , that is smaller than z0 .

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A sample path argument will prove that the busy periods are stochastically shorter with d d1 than they are with d d0 This means that for a xed buffer size, the truncating effect of nite buffers gets stronger as the cell-loss rate gets smaller The simulation experiments we report have cell-loss rates larger than 10 6 because the data do not have enough cells to reliably estimate cell-loss rates any smaller This result predicts that if we could do experiments with smaller cell-loss rates, the accuracy of Markov chain models would be better than the accuracy we are achieving now because the Markov chain models are at their best at small lags Consider two versions of the same buffer model, one with c I and the other with c < I.

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