Modeling Energy Consumption Based on Resource Utilization

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Povoa, Lucas Venezian ; Marcondes, Cesar ; Senger, Hermes ; Misra, S ; Gervasi, O ; Murgante, B ; Stankova, E ; Korkhov, V ; Torre, C ; Rocha, AMAC ; Taniar, D ; Apduhan, BO ; Tarantino, E

• Document type: Journal article
• Source: COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2019, PT I; v. 11619, p. 16-pg., 2019-01-01.

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. (AU)

• FAPESP's process: 18/22979-2 - IoT-SED: security and efficiency in data transport on Internet of Things
• Grantee: Daniel Macêdo Batista
• Support Opportunities: Regular Research Grants
• FAPESP's process: 18/00452-2 - Supporting scalablility and efficiency for scientific applications
• Grantee: Hermes Senger
• Support Opportunities: Regular Research Grants