  We investigate the duet measurement procedure, which helps improve the
accuracy of performance comparison experiments conducted on shared machines by
executing the measured artifacts in parallel and evaluating their relative
performance together, rather than individually. Specifically, we analyze the
behavior of the procedure in multiple cloud environments and use experimental
evidence to answer multiple research questions concerning the assumption
underlying the procedure. We demonstrate improvements in accuracy ranging from
2.3x to 12.5x (5.03x on average) for the tested ScalaBench (and DaCapo)
workloads, and from 23.8x to 82.4x (37.4x on average) for the SPEC CPU 2017
workloads.
