Consider a mapping project in which overlap of clonal segments is inferred from complete multiple restriction digests. The fragment sizes of the clones are measured with some error, potentially leading to a map with erroneous links. The number of errors in the map depends on the number and types of enzymes used to characterize the clones. The most critical parameter is the decision rule k, or the criterion for declaring clone overlap. Small changes in k may cause an order of magnitude change in the amount of work it takes to build a map of given completion. We observe that the cost of an optimal mapping strategy is approximately proportional to the target size. While this finding is encouraging, considerable effort is nonetheless required: for large-scale sequencing projects with up-front mapping, mapping will be a non-negligible fraction of the total sequencing cost.

We present a capacity planning case study showing a significant opportunity for improving the utilization of a large, low-latency, highly available online service containing 100K+ servers spanning 9 geographic regions. Analyzing 30 PB of traces over 90 days we devised a new iterative black-box capacity planning model using the discovered relationships between workload, utilization, and quality. We verified the model on 1,000s of servers showing capacity reductions between 20% and 40% with effectively no impact on workload latency, availability, or the capacity required for disaster recovery. These results are confirmed experimentally by shrinking production server pools to cause the remaining servers to run at higher utilization, and using data from real-world large scale unplanned failures. Finally, we show examples of using our model for offline regression analysis to detect critical issues before their deployment.

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