Experimental Evaluation Methodology for the Era of No Steady Performance

Recent studies of virtual machine warm up have pointed out that even small deterministic microbenchmarks executed in tightly controlled circumstances often do not reach a steady state of peak performance. This impacts performance evaluation methodologies that focus on performance after warm up, because the lack of a steady state may violate common assumptions made when computing metrics such as the average performance or the confidence interval for that average.

Our work examines the reported lack of steady state in the context of comparatively larger virtual machine workloads. We document and analyze similar lack of steady state and argue that it should be considered an inherent property of these workloads rather than a fault. We introduce an updated performance evaluation methodology for workloads whose execution exhibits segments of steady state performance separated by sudden performance changes. Using the Renaissance benchmark suite for the Java Virtual Machine, we show that the methodology can produce confidence intervals that miss the true performance over 20 % less often than the existing methodologies.