A Measurement-based Characterization of the Energy Consumption in Data Center Servers
In this work we present an exhaustive empirical characterization of the power requirements of multiple components of data center servers. To do so, we devise different experiments to stress these components, taking into account the multiple available frequencies and the fact that we are working with...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2015 |
| País: | España |
| Institución: | IMDEA Networks Institute |
| Repositorio: | IMDEA Networks Institute Digital Repository |
| Idioma: | inglés |
| OAI Identifier: | oai:dspace.networks.imdea.org:20.500.12761/107 |
| Acceso en línea: | http://hdl.handle.net/20.500.12761/107 https://dx.doi.org/http://dx.doi.org/10.1109/JSAC.2015.2481198 |
| Access Level: | acceso abierto |
| Palabra clave: | Cloud Computing CPU data centers disk DVFS energy efficiency energy measurements network |
| Sumario: | In this work we present an exhaustive empirical characterization of the power requirements of multiple components of data center servers. To do so, we devise different experiments to stress these components, taking into account the multiple available frequencies and the fact that we are working with multicore servers. In these experiments, we measure energy consumption of server components and identify their optimal operational points. Our study proves that the curve defining the minimal CPU power utilization, as a function of the load in Active Cycles Per Second, is neither concave nor purely convex. Instead, it definitively shows a superlinear dependence on the load. Similarly, we present results on how to improve the efficiency of network cards and disks. Finally, we validate the accuracy of the model derived from our characterization by comparing the real energy consumed by two Hadoop applications?PageRank and WordCount?with the estimation from our model, obtaining errors below 4:1% on average. |
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