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of cells or bursts, but admission control and congestion control are suf ciently shortterm that we propose focusing on the transient behavior of the aggregate demand process In fact, even for capacity planning the transient analysis plays an important role The transient analysis determines how long it takes to recover from rare congestion events One application we have in mind is that of networks carrying rate-adaptive traf c In this case the bandwidth process could represent the ideal demand of a source, even though it is able to function when allocated somewhat less bandwidth So from the point of view of quality, excursion of aggregate bandwidth demand above available supply may be acceptable in the short-term, but one would want to dimension the link so that such excursions are suf ciently short-lived.



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In this or other contexts, if the recovery time from overload is relatively long, then we may elect to provide extra capacity (or reduce demand) so that overload becomes less likely However, we do not focus speci cally on actual design and control here; see Duf eld and Whitt [14] for some speci c examples Our main contribution here is to show how the transient analysis for design and control can be done The remainder of this chapter is devoted to showing how to do transient analysis with the source traf c model We suggest focusing on the future time-dependent mean conditional on the present state The present state of each level process consists of the level and age (elapsed holding time in that level).





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You can attempt to strike a balance between transaction size and recovery time Consider the difference between a transaction that updates every customer record in a single SQL statement and one that walks through the index sequentially, updating each record as a separate transaction Clearly, the second approach takes longer, as it eliminates any benefits of caching and bulk data retrieval that the database might perform for you Large transactions are the norm, but they aren t always the easiest to recover Database crashes between separate transactions mean the client program can pick up where it left off, possibly repeating the one transaction that was inflight at failure time This requires that client-side applications know how to walk through the table, and also how to determine what the pickup point is; a client-side crash will require the application to query the database to find its last update.

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Because of the anticipated large number of sources, the actual bandwidth process should be closely approximated by its mean, by the law of large numbers (LLN) As in Duf eld and Whitt [14], the conditional mean can be thought of as a deterministic uid approximation; for example, see Chen and Mandelbaum [8] Since the withinlevel variation process has mean zero, the within-level variation process has no effect on this conditional mean Hence, the conditional mean of the aggregate bandwidth process is just the sum of the conditional means of the component level processes Unlike the more elementary M =G=I model considered in Duf eld and Whitt [14], however, the conditional mean here is not available in closed form In order to rapidly compute the time-dependent conditional mean aggregate demand, we exploit numerical inversion of Laplace transforms.

Involving the client in the reliability design makes recovery easier but makes the client more complex It also requires you have the political clout to get the application designers to sit down and consider recovery issues when doing their design If you re successful, you ll have an easier time in the data center and might consider using your newly found spare time to run for public office Another independent approach is to trim the logs more frequently This process, called checkpointing, forces the database to verify that any transaction represented in the logs is written out to disk, and then to discard the related log file entries.

It follows quite directly from the classical theory of semi-Markov process that explicit expressions can be given for the Laplace transform of the conditional mean More recently, it has been shown that numerical inversion can be an effective algorithm; see Abate et al [1] For related discussions of transient analysis, design and control, see s 13, 16, and 18 in this volume 173 OUTLINE OF THE CHAPTER.

It is crucial to map out your speci c business goals before embarking on a social media program. As Yogi Berra put it, If you don t know where you are going, you will wind up somewhere else.

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