@Peter R - Excellent work on 2 of 5 - thanks due to you and your co-authors for this study.
My day gig is as a research technologist at one of the largest competing equipment manufacturers. I would like to speak of a subsystem metric that may give more insights into total system performance.
After being stalled at a plateau for one or more decades, data storage has gotten orders of magnitude faster over the last few years. We've gone from HDDs to storage bus SSDs to SSDs on the system local PCIe bus (instead of attached via FC, IP, SAS or SATA), and now we are looking at memory technologies orders of magnitude faster than NAND Flash. Up and coming storage devices will have IO latencies lower than processor context switch times.
With such fast devices, we are learning that measurements made upon the storage stack have less and less correlation with actual system-wide responsiveness. Accordingly, the industry is starting to measure IO latency in a different way - one which provides a ready metric to compare given storage solutions' real effects upon total system performance.
Specifically, we are learning that the latency outliers really come to dominate overall system performance. At least in our little area of computing performance.
In order to bring order to this understanding, we are now employing a new metric that is essentially '# of nines' on the X-axis (independent variable), and time on the Y axis (dependent variable). In this, '# of nines' is the proportion of IOs that complete within the time on that data point's Y-axis. So 90% of all IOs complete within some time, 99% of all IOs complete within some larger time, 99.9% complete within some time larger yet... This yields a monotonically nondecreasing data series. The increase tending to appear 'exponential' on such a graph - to some '# of nines' anyway.
Without access to your data, I don't know if there is value in this approach for your study. However, in the data storage field, this has allowed us a tool to get a handle on overall 'speed' of the behavior of very complex systems, from some subsystem measurements that we can actually instrument.
It might be interesting to run these figures for your data to see what new insights might be available.