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When talking about asynchronous queuing systems, I often point out that they work best in optimistic designs that is, in cases where you try to cover the average case instead of the oddball but serious failure mode. After I used the term in a conversation, a customer asked me, What do you mean by asynchronous but optimistic transactional systems It s a model that was popularized inside of Sun by Alfred Chuang (who went on to become the A in BEA Systems), but the examples seemed particular to Sun and computer sales operations. Here s the example I used to explain asynchronous operation: Consider how you and your spouse manage your checkbook. In a synchronous world, you would write checks together, go to the ATM together, and even balance the checkbook register together in a true fit of marital bliss. This model is impractical because it assumes you re physically together and in total agreement for every single financial transaction. In real life, you operate asynchronously; you each visit the ATM separately and write your own checks, confident that your spouse won t suddenly withdraw 99 percent of your available funds and head for Las Vegas. Your ability to handle your own slice of the financials is the asynchronous part; the assurance that most transactions don t produce exceptions is the optimistic part; the monthly discussion over who gets to sort through receipts and checks to balance the checkbook and produce the truth is the transactional part. Eventually, all assumptions are cleared and exceptions processed, and you re back running asynchronously again. Hal



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[31] also provide initial evidence that models allowing for a more complete description of network traf c, in particular its multifractal behavior, typically outperform additive Gaussian models in the context of speci c performance problems [26] 205 CONCLUSION.





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Applications that can operate optimistically while recovering an asynchronous queue are ideal. It s possible to execute ahead, with the optimistic assumption that transactions in the queue won t interfere with or invalidate transactions being currently processed. As the queue of transactions is applied, those assumptions are tested and any exception conditions are handled, allowing the system to continue functioning even while it performs recovery. This is vaguely similar to a disk mirroring system that does disk rebuild after hot-spare replacement on the fly. The system makes the optimistic assumption that you won t suffer another failure while it is copying data onto the new disk, and it allows the mirror repair to proceed asynchronously with respect to the other disk activity that is generating independent I/O operations. Be careful not to leave single points of failure when designing with queues. If you leave the receiving queue on the same machine as your primary database, but have the queue write to two intermediate queues on other machines, you re exposed if one of the intermediate machines crashes and leaves the queue inactive during the downtime. See Figure 18.4 for an example of two databases that populate an intermediate queue used to replicate the transaction stream into two disparate databases. If the intermediate node fails without any redundancy, transactions in the queue are left in limbo until the node is repaired. When using asynchronous queues, keep the queues close to the databases that they re responsible for updating so that they fail and recover consistently with the database servers.

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One of the implications of the discovery of self-similar or multifractal scaling behavior in measured network traf c has been the realization that network traf c modeling and performance analysis can and should no longer be viewed as exercises in data tting and queueing theory or simulations. Instead, relevant traf c modeling

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has become a natural by-product of a renewed effort that aims at gaining a physical (ie, network-related) understanding of the empirically observed scaling phenomena Moreover, the novel insights gained from such a physical-based understanding of actual network traf c dynamics often allows for a qualitative assessment of their potential impact on network performance, when more quantitative methods appear to be mathematically intractable or are not yet available While traditional performance modeling has mainly lived in the con nes of mathematically tractable queueing models, the observed scaling properties of measured network traf c and the constantly changing nature of today's networks strongly suggest a shift away from focusing exclusively on quantitative methods for assessing the wide range of network performance-related problems toward achieving instead a more qualitative understanding of the implications of the dominant features of measured network traf c on relevant networking issues.

In the remaining portion of this chapter, we discuss some other thoughts and issues relative to replication.

While supporting such a qualitative knowledge where possible through a quantitative analysis is clearly desirable, we believe that the development of an ubiquitous, stable, robust, and high-performance networking infrastructure of the future will depend crucially on a qualitative rather than quantitative understanding of networks and network traf c dynamics Finally, in terms of practical relevance, we also argue that by incorporating via multifractals local scaling characteristics of the traf c into a workload model, it may become in fact feasible to adequately describe traf c in a closed system (like the Internet) with an open model The vast majority of currently used models for network traf c completely ignore the fact that the dynamic nature of packet traf c over a given link is the result of a combination of source=user behavior and highly nonlinear interactions between the individual users and the network.

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