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There s another balance between performance and recovery in setting the checkpoint interval: A longer checkpoint interval means that you can survive higher peaks of logging activity; a shorter interval means that you clamp the recovery window based on the depth of the log file If you build in sufficient disk I/O capacity, and the database storage can handle the activity using whatever CPU and memory resources are required, more frequent checkpointing will be a win in the longer run..



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Does anyone know if there is away of getting this printer to run on Windows 10 using wifi. I had it running on same computer with windows 7.

The rest of this chapter is organized as follows. In Section 17.4, we show that the Laplace transform of the mean of the transient conditional aggregate demand can be expressed concisely. This is the main enabling result for the remainder of the chapter. The conditional mean itself can be very ef ciently computed by numerically inverting its Laplace transform. To carry out the inversion, we use the Fourier-

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series method in Abate and Whitt [2] (the algorithm Euler exploiting Euler summation), although alternative methods could be used The inversion algorithm is remarkably fast; computation for each time point corresponds simply to a sum of 50 terms We provide numerical examples in Examples 1762 and 1781 Example 1781 is of special interest, because the level-holding-time distribution there is Pareto In Section 175 we show that in some cases we can avoid the inversion entirely and treat much larger models We can avoid the inversion if we can assume that the level holding times are relatively long compared to the times of interest for control Then we can apply a single-transition approximation, which amounts to assuming that the Markov chain is absorbing after one transition Then the conditional mean is directly expressible in terms of the level-holding-time distributions.

You ll probably want to try smaller transactions and more frequent checkpointing. Both introduce minimal complexity and don t require major serverside changes. The next step is to use a parallel database to improve recovery time, but this, too, comes with a performance consideration.

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Alternatively, we can perform a two-transition approximation, which only involves one-dimensional convolution integrals In Section 176 we describe the value of having more detailed state information, speci cally the current ages of levels With heavy-tailed distributions, a large elapsed holding time means that a large remaining holding time is very likely; for examples see Duf eld and Whitt [14, Section 8] for background, and Harchol-Balter and Downey [21] for an application in another setting In Section 177 we turn to applications to capacity planning The idea is to approximate the probability of an excursion in demand using Chernoff bounds and other large deviation approximations, then chart its recovery to a target acceptable level using the results on transience Interestingly, the time to recover from excursions suf ciently close to the target level depends on the level durations essentially only through their mean.

Correspondingly, the conditional mean demand relaxes linearly from its excursion, at least approximately so, for suf ciently small times If the chance for a larger excursion is negligible (as determined by the large deviation approximation mentioned) then this simple description may suf ce An example is given in Section 178 In Section 179 we show how long-range dependence in the level process arises through heavy-tailed level-holding-time distributions Finally, we draw conclusions in Section 1710..

Parallel databases like Oracle Real Application Clusters (RACs) use multiple database engines on a single, shared disk set or on multiple, independent (shared nothing) disk units The goal is to provide a single view of a large database with more than one host, operating system, and database engine underneath it for performance and reliability Note that a parallel database in this context is not just a database that parallelizes activity using the operating system s threads model; that form of parallelization is expected and provides a solid performance improvement We re going to look at the case where you have multiple servers and multiple database engines, and have to coordinate availability and performance between them Scaling up with multiple engines sounds nice in theory, but the drawback to parallel databases is coordinating inserts, updates, and cache information between the instances.

17.4 17.4.1

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