Cloud data centers commonly use virtualization technologies to provision compute capacity with a level of indirection between virtual machines and physical compute resources. In this paper we explore the use of that level of indirection asa means for autonomic data center configuration optimization and propose a sensor-actuator model to capture optimization-relevant relationships between data center events, monitored metrics (sensors data), and management actions (actuators). The model quantifies and characterizes a wide spectrum of actions to help identify the suitability of different actions in specific situations, and outlines what (and how often) data needs to be monitored to capture, classify, and respond to events that affect the performance of data center operations. To support the analysis and illustrate the utility of the model, the paper also details a set of testbed experiments aimed to characterize trade-offs in the use of different actions in infrastructure optimization.