Modelling Energy Consumption based on Resource Utilization
This addresses the costly and complex issue of power management for datacenters and clusters, though it is incremental as it applies existing machine learning methods to a new application.
The paper tackled the problem of estimating energy consumption in large computational infrastructures by using resource utilization data as proxies, achieving a model with 99.94% accuracy and 6.32 watts error in the best case.
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.