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This example only considers one type of outage. There are, of course, any number of failures that can cause system downtime, and each of them has its own set of protective measures. For many failures, proper protection will mean zero downtime for example, the failure of a disk in a system where disks are protected with RAID technology, such as disk mirroring. For a detailed discussion of RAID technology, please refer to 7, Highly Available Data Management.

Re s ! 0:



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Figure 3.7 shows that outages don t only reach forward in time; they can also reach backwards and affect data that users have already entered and believed was committed. Sometimes the data may have been acted upon by other processes. For example, an e-commerce site could bill a customer s credit card and then lose the data that would have permitted them to ship product to the customer. That s bad for everybody.

6:29

Indeed, P V is zero with probability 1 r, and it equals a residual busy period P* with probability r. Combination of Eqs. (6.28) and (6.29) yields E e sP V E exp that is, E e sP V satis es k s E exp s k s V 1 r and jk s j 1; Re s ! 0: 6:31 s E e sP V V 1 r ; Re s ! 0; 6:30

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Wondershare PDFelement OCR Plugin
After enabling the OCR feature in PDFelement Pro , all features will be applicable to scanned PDFs. Open a scanned PDF or image-based PDF, a message will appear on the information bar. Click on the "Perform OCR ", or you can go to the "Convert" tab and click the " OCR " button to perform the OCR feature.

It may be shown [20] that these equations have one and only one solution for Re s ! 0 with k 0 1; and k s is regular for Re s > 0, continuous for Re s ! 0, and decreasing in s for s real. Using Theorem 6.3.1, the heavy traf c limit theorem for the waiting time W in the M =G=1 FCFS queue (or equivalently for the workload V ), one can now prove the following heavy traf c limit theorem for P V , or equivalently, see Eq. (6.29), for P*. Theorem 6.3.7. For the stable M =G=1 queue with regularly varying service time distribution of index n, as speci ed in Eq. (6.2), the ``contracted'' busy period DP r P V converges in distribution for r 4 1. The limiting distribution Pn 1 t is speci ed by the fact that its LST p r is that zero of the equation y 1 ; 1 bry n 1 Re r ! 0; 6:32

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How it works. We reverse engineer older scanner drivers so that you can use old scanners that are no longer supported. We also use the latest drivers and plugins from manufacturers and operating systems. You download VueScan and use it to scan documents, photos, film and slides with your scanner.

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The lost data duration reaches backwards in time from Time T1 to Time T0, the time of the last relevant data protection event Depending on the nature of the outage, that event could be the most recent tape backup, data replication, or mirrored disk write As with downtime, though, there are technologies that can protect critical systems against lost data; the type of frequency of the data protection events will ultimately determine how much data will be lost in an outage Some of these technologies, once again using a lost data center as an example, are shown in Figure 38 In this case, many of the same technologies that can reduce downtime also reduce lost data That will not be the case for all types of outages The technical term for lost data is Recovery Point Objective, or RPO.

with the property that y 0 1; and the coef cient of contraction DP r equals 1 r DW r . The same results hold for DP r P*, the ``contracted'' residual busy period. REMARK 6.3.8. In Cohen [20] it is shown that Theorem 6.3.7 also holds for n 2.

We touched on ratings in 5 in the section called Stars in Their Eyes. It bears repeating that a simple system set up to monitor uctuations in ratings across multiple ratings sites can prove useful. We re not looking for absolute scores here, just directional information that gives you a clue when something happens. If your scores suddenly skyrocket, you should be aware. Should they plummet, you should be very aware.

The technology choices that can affect the amount of lost data after the loss of a data center are as follows: Synchronous replication Under synchronous replication, data arrives at the secondary host as soon as it is written to the primary data center The downside to synchronous replication is in performance; since data must be sent across the network to the remote site, and then confirmation must come back, for each write, performance can be seriously impacted More information on replication can be found in 18 Asynchronous replication With asynchronous replication, you get back the performance that you lose with synchronous replication, but in return, a small number of transactions may be delayed in being sent to the secondary site.

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