Modelling Energy Consumption based on Resource Utilization

TL;DR

This paper proposes using resource utilization metrics and machine learning models to accurately estimate energy consumption in large computational systems, reducing the need for direct power measurements.

Contribution

It introduces a novel approach that leverages common OS metrics and machine learning to predict power consumption with high accuracy, simplifying energy management.

Findings

Achieved 99.94% accuracy in power estimation

Best case error of 6.32 watts

Effective across different hardware configurations

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.