2017/09/15 by Lucas Venezian Povoa, Povoa, Lucas Venezian, César Marcondes +3
Computer Science · #Cloud Computing and Resource Management #Data Stream Mining Techniques #Distributed #FOS: Computer and information sciences #Machine Learning (cs.LG) #Parallel #Recommender Systems and Techniques #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.1709.06076
openalex publication_date 2017/09/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.