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Reducing energy consumption has a significant role in mitigating the total cost of ownership of computing clusters. Building heterogeneous clusters by combining high-end and low-end server nodes (e.g., Xeons and Atoms) is a recent trend towards achieving energy-efficient computing. This requires a cluster-level power manager that has the ability to predict future load, and server nodes that can quickly transition between active and low-power sleep states. In practice however, the load is unpredictable and often punctuated by spikes, necessitating a number of extra “idling” servers. We design a cluster-level power manager that (1) identifies the optimal cluster configuration based on the power profiles of servers and workload characteristics, and (2) maximizes work done per watt by assigning P-states and S-states to the cluster servers dynamically based on current request rate. We carry out an experimental study on a web server cluster composed of high-end Xeon servers and low-end Atom-based Netbooks and share our findings.