Making Cluster Applications Energy-Aware
Power consumption has become a critical issue in large scale clusters. Existing solutions for addressing the servers' energy consumption suggest ``shrinking'' the set of active machines, at least until the more power-proport-ional hardware devices become available. This paper demonstrates that leveraging the sleeping state, however, may lead to unacceptably poor performance and low data availability if the distributed services are not aware of the power management's actions. Therefore, we present an architecture for cluster services in which the deployed services overcome this problem by actively participating in any action taken by the power management. We propose, implement, and evaluate modifications for the Hadoop Distributed File System and the MapReduce clone that make them capable of operating efficiently under limited power budgets.