Dynamic voltage and frequency scaling (DVFS) and virtual machine (VM) based server consolidation are well-known CPU scaling techniques for energy conservation that can have an adverse impact on system performance. For the responsiveness-sensitive multitier applications running in today's data centers, queuing models should ideally be used to predict the impact of CPU scaling on response time, to allow appropriate runtime trade-offs between performance and energy use. In practice, however, such models are difficult to construct and thus are often abandoned for ad-hoc solutions. In this paper, an alternative measurement-based approach that predicts the impact without requiring detailed application knowledge is presented. The approach proposes a new predictive model, the CPU gradient, that can be automatically measured on a running system using lightweight and nonintrusive CPU perturbations. The practical feasibility of the approach is demonstrated using extensive experiments on multiple multitier applications, and it is shown that simple energy controllers can use gradient predictions to derive as much as 50% energy savings while still meeting response time constraints.