Abstract
To evaluate energy use in green clusters, power models take the resource utilization data as the input to predict server power consumption. We propose a novel method in power modeling combining a global linear model and a local approximation model. The new model enjoys high accuracy by compensating the global linear model with local approximation and exhibits robustness with the generalization capability of the global regression model. Empirical evaluation demonstrates that the new approach outperforms the two existing approaches to server power modeling, the linear model and the k-nearest neighbor regression model.
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Notes
Available at http://www.mersenne.org/download/.
Available at http://www.netlib.org/linpack/.
Available at https://www.spec.org/power_ssj2008/.
Available at http://www.iometer.org/.
Available at http://tu-dresden.de/zih/firestarter/.
The tool LibSVM (available at https://www.csie.ntu.edu.tw/~cjlin/libsvm/) is used for support vector machines. The tool Weka (available at http://www.cs.waikato.ac.nz/ml/weka) is used for logistic regression and random forests.
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Acknowledgements
We thank Rahul Khanna, Honesty Young, and Shilin Wang for their comments on an early draft of the paper.
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Du, X., Li, C. Combining global regression and local approximation in server power modeling. SICS Softw.-Inensiv. Cyber-Phys. Syst. 34, 35–43 (2019). https://doi.org/10.1007/s00450-018-0391-x
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DOI: https://doi.org/10.1007/s00450-018-0391-x