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Including Variability in Large-Scale Cluster Power Models

John D. Davis, Suzanne Rivoire, Moises Goldszmidt, and Ehsan K. Ardestani


Studying the energy efficiency of large-scale computer systems requires models of the relationship between resource utilization and power consumption. Prior work on power modeling assumes that models built for a single node will scale to larger groups of machines. However, we find that inter-node variability in homogeneous clusters leads to substantially different models for different nodes. Moreover, ignoring this variability will result in significant prediction errors when scaled to the cluster level. We report on inter-node variation for four homogeneous five-node clusters using embedded, laptop, desktop, and server processors. The variation is manifested quantitatively in the prediction error and qualitatively on the resource utilization variables (features) that are deemed relevant for the models. These results demonstrate the need to sample multiple machines in order to produce accurate cluster models.


Publication typeArticle
Published inComputer Architecture Letters
PublisherIEEE Computer Society
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