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Humans are emotional creatures. We pay attention to the emotions of other humans. We respond in terms of ight, ight, or mate. Everything else is psycho- and socialconditioning cobbled on top so we can get along better in ever-increasing numbers in ever-decreasing spaces. The marketing professional is tasked with tapping into this lizard-brain, gut-reaction, psychological quagmire and captivating us through their powers of persuasion. Some have gotten very good at it. Some have used higher-order statistical analytics to target us with just the right message at just the right time. But they can t compete with my brother Doug. The most compelling discount offer, the most heartfelt celebrity endorsement or the most powerful, massive, and expensive marketing campaign cannot survive an ever-soslight frown on my brother Doug s face when I m shopping for dress shirts. Doug has exquisite taste in dress shirts. On the one hand this is a shame as I ve seen some really great shirts I wanted to get him as gifts but simply would not presume to choose on his behalf. He has style, he has taste, and he dresses in an elegantly casual way that communicates breeding and panache without any pomposity or arrogance whatsoever. Sartorially speaking, if Doug says it s good, it s golden. Whether he tells



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Six-sigma methodology is another popular trend that drives us to be datafocused and process-intense. The heart of six-sigma methodology is to measure something, find out where defects are being introduced, and then remove the source of the defects so that the resulting process shows less than six defects per million opportunities for a defect (six sigmas or standard deviations away from the mean). Though this methodology is most commonly used for manufacturing processes and hard goods, it has applicability to reliability of networked systems as well. Instead of thinking about six sigma as a searchand-destroy process for defects, think about it as a way of reducing variation.





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Starting with the time-of-day, user behavior may be a function of network congestion leading to self-regulation (a user may choose to continue his Web sur ng activities at a later time if overall response time is exceedingly high, a form of backoff), congestion pricing may assign costs above and beyond those exacted by performance degradation, users may switch between different service classes in a multiservice network [10, 20], users may perform network access and control decisions cooperatively or sel shly leading to a noncooperative network environment characteristic of the Internet, users may observe behavioral patterns when navigating the Web, and so forth The challenge lies in identifying robust, invariant behavioral traits possibly exhibiting scaling phenomena and quantifying their in uence on network performance.

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Scaling Phenomena in Network Architecture The recent discovery of power law scaling in network topology [22] points toward the fact that scaling may not be limited to network traf c and system workloads On the other hand, power law scaling in the connectivity structure of the Internet stretches the meaning of ``workload characterization'' if it is to be included under the same umbrella More importantly, it is unclear whether the diffusive connectivity structure implied by power laws affects temporal traf c properties and network performance in unexpected, nontrivial ways.

Renewability, and the statistics that support it, are covered in exquisite detail by Paul Tobias and David Trindade in Applied Reliability, published by Kluwer Academic Publishers (1995). The math is intense but complete. If you want to explore any of the concepts of MTBF, and the more proper ideas of failure rates and incidents over time, immerse yourself in their work.

For example, routing in graphs with exponential scaling in their connectivity structure is different from routing in graphs with power law scaling, but that is not to say that this has implications for traf c characterization and performance above and beyond its immediate scope of in uence number of paths between a pair of nodes, their make-up, and generation of ``realistic'' network topologies for benchmarking If the distribution of link capacities were to obey a power law, then it is conceivable that this may exert a traf c shaping effect in the form of variable stretching-in-time of a transmission, which can inject heavy tailedness in transmission or connection duration that is not present in the original workload The challenge in architectural characterization lies in identifying robust, invariant properties exhibiting scaling behavior and relating these properties to network traf c, load, and performance where a novel and robust relationship is established.

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What are the values that users find most critical in your systems Response time Consistent behavior Correct behavior These are the critical-to-quality (CTQ) variables that you can measure. Define failures, or defects, based on these CTQs. If a transaction is expected to complete in 10 seconds, and it runs for 30 seconds but eventually completes correctly, is that a failure Is it a defect Can you relate these user CTQs to components in the system Where are the defects introduced What are the sources of variation, and how can you control those system components by changing their availability characteristics

An integral component of workload modeling is synthetic workload generation. In many instances, in particular, those where the workload model is constructive in

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