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Transaction processing monitors (TPMs), now sometimes referred to as transactional middleware, are your best bet for hard, reliable database replication. They re also the slowest and hardest to implement, a trade-off you d expect for a solid guarantee. You can use some distributed transaction features of some databases to accomplish the same results described in this section, with the same upsides and drawbacks. Two-phase commit (see Figure 18.3) ensures that two databases are updated in a consistent manner transaction by transaction. The TPM, or resource manager, makes sure that both systems can proceed with the transaction, then it applies the transaction to each, verifies completion, and completes the higherlevel transaction it is managing for the application. If either side fails to complete the transaction, the TPM can roll back the side that succeeded, aborting the transaction. TPMs do not require that the transactions be the same; they are commonly used to ensure that updates to multiple, different databases occur as a single transactional unit. The problem with two-phase commit is that it s slow. The TPM has to coordinate with each database, and every intermediate transaction has to complete before the application is notified of a successful transaction. So what is the advantage Recovery is no different than reconnecting to a single database. After all, the TPM was managing all updates in lockstep, so if the application sees the last transaction rolled back, it s been rolled back in all copies of the databases. You ll need to make sure your application uses the appropriate transaction interfaces supported by the TPM and databases, and this may require some application coding changes. You ll also need to make sure you can find the redundant copies of the database and reconnect the client application quickly.



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Note that the above generation of Kurtz's workload model that allows for multifractal within-session traf c rates is not consistent with measured data In fact, Feldmann et al [1] present empirical evidence that the observed within-session structure is itself a complicated mixture of additive and multiplicative components, and only by investigating network traf c at the TCP level (eg, in terms of port-to-port ows) is it possible to clearly isolate the multiplicative structure in measured network traf c Using the ndings from yet another empirical traf c study (see Feldmann et al [12]), we also know that the overall number of TCP connections per time unit exhibits selfsimilar scaling behavior for time scales on the order of seconds and beyond.





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Thus, to get a workload model for wide-area traf c that combines additive and multipicative structure and is consistent with measured data, we simply modify the multifractal version of Kurtz's process and require that (1) TCP connections arrive in accordance to a self-similar process, that is, the uctuations around the mean of the total number of TCP connection arrivals per time unit follow a FGN; (2) The TCP connections' workload is heavy tailed with in nite variance; and (3) the workload of a TCP connection is distributed over the connection's lifetime in a multifractal fashion, that is, according to a conservative binomial cascade.

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