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The effectiveness of open-loop control depends on how accurately it is possible to predict performance given the characteristics of variable rate ows. To discuss multiplexing options we make the simplifying assumption that ows have unambiguously de ned rates like uids, assimilating links to pipes and buffers to reservoirs. We also assume rate processes are stationary. It is useful to distinguish two forms of statistical multiplexing: bufferless multiplexing and buffered multiplexing. In the uid model, statistical multiplexing is possible without buffering if the combined input rate is maintained below link capacity. As all excess traf c is lost, the overall loss rate is simply E Lt c =E Lt , where Lt is the input rate process and c is the link capacity. It is important to note that this loss rate only depends on the stationary distribution of Lt and not on its time-dependent properties, including self-similarity. The latter do have an impact on other aspects of performance, such as the duration of overloads, but this can often be neglected if the loss rate is small enough. The level of link utilization compatible with a given loss rate can be increased by providing a buffer to absorb some of the input rate excess. However, the loss rate realized with a given buffer size and link capacity then depends in a complicated way on the nature of the offered traf c. In particular, loss and delay performance are very dif cult to predict when the input process is long-range dependent. The models developed in this book are, for instance, generally only capable of predicting asymptotic queue behavior for particular classes of long-range dependent traf c. An alternative to statistical multiplexing is to provide deterministic performance guarantees. Deterministic guarantees are possible, in particular, if the amount of data



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At the heart of the matter is defining the critical to quality metrics that can be extracted from an application, and then jointly developing the instrumentation, collection, and analysis tools with operations And if critical to quality rings a bell, it s the defining metric for six-sigma efforts, as discussed in 2, What to Measure, making your analytics data-driven and sensitive to variations in the data No matter how well designed something is, and despite all of the effort put into testing, verification, and logging, something is going to break You may get hit with something subtle, like a filesystem filling up such that your database can no longer log transactions, or you start dropping incoming mail.





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A t generated by a ow in an interval of length t satis es a constraint of the form: A t rt s. If the link serves this ow at a rate at least equal to r, then the maximum buffer content from this ow is s. Loss can therefore be completely avoided and delay bounded by providing a buffer of size s and implementing a scheduling discipline that ensures the service rate r [7]. The constraint on the input rate can be enforced by means of a leaky bucket, as discussed below. 16.3.2 The Leaky Bucket Traf c Descriptor

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Open-loop control in both ATM and Internet service models relies on the leaky bucket to describe traf c ows Despite this apparent convergence, there remain serious doubts about the ef cacy of this choice For present purposes, we consider a leaky bucket as a reservoir of capacity s emptying at rate r and lling due to the controlled input ow Traf c conforms to the leaky bucket descriptor if the reservoir does not over ow and then satis es the inequality A t rt s introduced above The leaky bucket has been chosen mainly because it simpli es the problem of controlling input conformity Its ef cacy depends additionally on being able to choose appropriate parameter values for a given ow and then being able to ef ciently guarantee quality of service by means of admission control.

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The leaky bucket may be viewed either as a statistical descriptor approximating (or more exactly, providing usefully tight upper bounds on) the actual mean rate and burstiness of a given ow or as the de nition of an envelope into which the traf c must be made to t by shaping Broadly speaking, the rst viewpoint is appropriate for stream traf c, for which excessive shaping delay would be unacceptable, while the second would apply in the case of (aggregates of) elastic traf c Stream traf c should pass transparently through the policer without shaping by choosing large enough bucket rate and capacity parameters Experience with video traces shows that it is very dif cult to de ne a happy medium solution between a leak rate r close to the mean with an excessively large capacity s, and a leak rate close to the peak with a moderate capacity [25].

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