Abstract
Power management is an expensive and important issue for large computational infrastructures such as datacenters, large clusters, and computational grids. However, measuring energy consumption of scalable systems may be impractical due to both cost and complexity for deploying power metering devices on a large number of machines. In this paper, we propose the use of information about resource utilization (e.g. processor, memory, disk operations, and network traffic) as proxies for estimating power consumption. We employ machine learning techniques to estimate power consumption using such information which are provided by common operating systems. Experiments with linear regression, regression tree, and multilayer perceptron on data from different hardware resulted into a model with 99.94% of accuracy and 6.32 watts of error in the best case.
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Notes
- 1.
Models were implemented in R (using RSNNS) and source code are available under the GNU General Public License version 3 at https://github.com/lucasvenez/ecm along with the employed dataset.
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Acknowledgement
Authors thank CAPES and RNP for partially supporting this research. Hermes Senger thanks CNPq (Contract Number 305032/2015-1) and FAPESP (Process numbers 2018/00452-2, and 2018/22979-2) for their support.
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Povoa, L.V., Marcondes, C., Senger, H. (2019). Modeling Energy Consumption Based on Resource Utilization. In: Misra, S., et al. Computational Science and Its Applications – ICCSA 2019. ICCSA 2019. Lecture Notes in Computer Science(), vol 11619. Springer, Cham. https://doi.org/10.1007/978-3-030-24289-3_18
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