  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.
