An analytical methodology to derive power models based on hardware and software metrics
Scholar | Otros documentos del autor: Dolz Zaragozá, Manuel Francisco; Kunkel, Julian; Chasapis, Konstantinos; Catalán Pallarés, Sandra
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TítuloAn analytical methodology to derive power models based on hardware and software metrics
Fecha de publicación2015-09
Versión del editorhttp://link.springer.com/article/10.1007/s00450-015-0298-8#
EditorSpringer Berlin Heidelberg
The use of models to predict the power con- sumption of a system is an appealing alternative to wattmeters since they avoid hardware costs and are easy to deploy. In this paper, we present a system ... [+]
The use of models to predict the power con- sumption of a system is an appealing alternative to wattmeters since they avoid hardware costs and are easy to deploy. In this paper, we present a systematic methodology to build models with a reduced number of features in order to estimate power consumption at node level. We aim at building simple power models by performing a per-component analysis (CPU, mem- ory, network, I/O) through the execution of four stan- dard benchmarks. While they are executed, we collect information from all the available hardware counters and resource utilization metrics provided by the sys- tem. Based on correlations among the recorded metrics and their correlation with the instantaneous power, our methodology allows i) to identify the significant met- rics; and ii) to assign weights to the selected metrics in order to derive reduced models. The reduction also aims at extracting models that are based on a set of hardware counters and utilization metrics that can be obtained simultaneously and, thus, can be gathered and com- puted on-line. The utility of our procedure is validated using real-life applications on an Intel Sandy Bridge architecture. [-]
Palabras clave / Materias
Cita bibliográficaDolz, M. F., Kunkel, J., Chasapis, K., & Catalán, S. (2016). An analytical methodology to derive power models based on hardware and software metrics. Computer Science-Research and Development, 31(4), 165-174.
Tipo de documentoinfo:eu-repo/semantics/article
Derechos de acceso
© Springer-Verlag Berlin Heidelberg 2015 © Springer International Publishing AG, Part of Springer Science+Business Media. "The final publication is available at Springer via http://dx.doi.org/10.1007/s00450-014-0267-7"
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