| Texto completo | |
| Autor(es): Mostrar menos - |
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
Número total de Autores: 13
|
| Tipo de documento: | Artigo Científico |
| Fonte: | COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2019, PT I; v. 11619, p. 16-pg., 2019-01-01. |
| Resumo | |
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) | |
| Processo FAPESP: | 18/22979-2 - IoT-SED: segurança e eficiência no transporte de dados na Internet das Coisas |
| Beneficiário: | Daniel Macêdo Batista |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 18/00452-2 - Aplicações científicas com suporte à escalabilidade e eficiência |
| Beneficiário: | Hermes Senger |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |