Reducing energy consumption of software is an increasingly important objective, and there has been extensive research for data centers, smartphones, and embedded systems. However, when it comes to software systems, we lack working tools and methodologies to directly reduce energy consumption. For performance, we can resort to configuration options for tuning response time or throughput. For energy, it is still unclear whether the underlying assumption that \emph{performance correlates with energy consumption} holds, especially when it comes to optimization via configuration. To evaluate whether and to what extent this assumption is valid for configurable software systems, we conducted the largest empirical study of this kind to date.

First, we searched the literature for reports on whether and why performance correlates with energy consumption. We found a mixed, even contradicting picture from positive to negative correlation, and that configurability has not been considered yet as a factor for this variance. Second, we measured and analyzed both the performance and energy consumption of $14$ real-world software systems. We found that, in many cases, it depends on the software system’s configuration whether performance and energy consumption correlate and that, typically, only few configuration options influence the degree of correlation. A fine-grained analysis at the function level revealed that only few functions are relevant to obtain an accurate proxy for energy consumption and that, knowing them, allows us to infer individual transfer factors between performance and energy consumption.