Tarazu: optimizing MapReduce on heterogeneous clusters
ASPLOS XVII Proceedings of the seventeenth international conference on Architectural Support for Programming Languages and Operating Systems
An energy management system for cluster infrastructures
Computers and Electrical Engineering
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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.