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Cloud providers possessing large quantities of spare capacity must either incentivize clients to purchase it or suffer losses. Amazon is the first cloud provider to address this challenge, by allowing clients to bid on spare capacity and by granting resources to bidders while their bids exceed a periodically changing spot price. Amazon publicizes the spot price but does not disclose how it is determined.By analyzing the spot price histories of Amazon's EC2 cloud, we reverse engineer how prices are set and construct a model that generates prices consistent with existing price traces. We find that prices are usually not market-driven as sometimes previously assumed. Rather, they are typically generated at random from within a tight price interval via a dynamic hidden reserve price. Our model could help clients make informed bids, cloud providers design profitable systems, and researchers design pricing algorithms.

Science is based upon observation. The scientific study of complex computer systems should therefore be based on observation of how they are used in practice, as opposed to how they are assumed to be used or how they were designed to be used. In particular, detailed workload logs from real computer systems are invaluable for research on performance evaluation and for designing new systems.Regrettably, workload data may suffer from quality issues that might distort the study results, just as scientific observations in other fields may suffer from measurement errors. The cumulative experience with the Parallel Workloads Archive, a repository of job-level usage data from large-scale parallel supercomputers, clusters, and grids, has exposed many such issues. Importantly, these issues were not anticipated when the data was collected, and uncovering them was not trivial. As the data in this archive is used in hundreds of studies, it is necessary to describe and debate procedures that may be used to improve its data quality. Specifically, we consider issues like missing data, inconsistent data, erroneous data, system configuration changes during the logging period, and unrepresentative user behavior. Some of these may be countered by filtering out the problematic data items. In other cases, being cognizant of the problems may affect the decision of which datasets to use. While grounded in the specific domain of parallel jobs, our findings and suggested procedures can also inform similar situations in other domains.

As parallel jobs get bigger in size and finer in granularity, "system noise" is increasingly becoming a problem. In fact, fine-grained jobs on clusters with thousands of SMP nodes run faster if a processor is intentionally left idle (per node), thus enabling a separation of "system noise" from the computation. Paying a cost in average processing speed at a node for the sake of eliminating occasional processes delays is (unfortunately) beneficial, as such delays are enormously magnified when one late process holds up thousands of peers with which it synchronizes.We provide a probabilistic argument showing that, under certain conditions, the effect of such noise is linearly proportional to the size of the cluster (as is often empirically observed). We then identify a major source of noise to be indirect overhead of periodic OS clock interrupts ("ticks"), that are used by all general-purpose OSs as a means of maintaining control. This is shown for various grain sizes, platforms, tick frequencies, and OSs. To eliminate such noise, we suggest replacing ticks with an alternative mechanism we call "smart timers". This turns out to also be in line with needs of desktop and mobile computing, increasing the chances of the suggested change to be accepted.

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