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Campo DC | Valor | Lengua/Idioma |
---|---|---|
dc.contributor.advisor | Nesmachnow, Sergio | - |
dc.contributor.author | Muraña, Jonathan | - |
dc.date.accessioned | 2020-12-29T16:49:22Z | - |
dc.date.available | 2020-12-29T16:49:22Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Muraña, J. Empirical characterization and modeling of power consumption and energy aware scheduling in data centers [en línea] Tesis de maestría. Montevideo : Udelar. FI. INCO : PEDECIBA, 2019. | es |
dc.identifier.issn | 1688-2792 | - |
dc.identifier.uri | https://hdl.handle.net/20.500.12008/26248 | - |
dc.description.abstract | Energy-efficient management is key in modern data centers in order to reduce operational cost and environmental contamination. Energy management and renewable energy utilization are strategies to optimize energy consumption in high-performance computing. In any case, understanding the power consumption behavior of physical servers in datacenter is fundamental to implement energy-aware policies effectively. These policies should deal with possible performance degradation of applications to ensure quality of service. This thesis presents an empirical evaluation of power consumption for scientific computing applications in multicore systems. Three types of applications are studied, in single and combined executions on Intel and AMD servers, for evaluating the overall power consumption of each application. The main results indicate that power consumption behavior has a strong dependency with the type of application. Additional performance analysis shows that the best load of the server regarding energy efficiency depends on the type of the applications, with efficiency decreasing in heavily loaded situations. These results allow formulating models to characterize applications according to power consumption, efficiency, and resource sharing, which provide useful information for resource management and scheduling policies. Several scheduling strategies are evaluated using the proposed energy model over realistic scientific computing workloads. Results confirm that strategies that maximize host utilization provide the best energy efficiency. | es |
dc.description.sponsorship | Agencia Nacional de Investigación e Innovación FSE_1_2017_1_144789 | es |
dc.format.extent | 111 p. | es |
dc.format.mimetype | application/pdf | es |
dc.language.iso | en | es |
dc.publisher | Udelar.FI. | es |
dc.rights | Las obras depositadas en el Repositorio se rigen por la Ordenanza de los Derechos de la Propiedad Intelectual de la Universidad de la República.(Res. Nº 91 de C.D.C. de 8/III/1994 – D.O. 7/IV/1994) y por la Ordenanza del Repositorio Abierto de la Universidad de la República (Res. Nº 16 de C.D.C. de 07/10/2014) | es |
dc.subject | Green computing | es |
dc.subject | Energy efficiency | es |
dc.subject | Multicores | es |
dc.subject | Energy model | es |
dc.subject | Cloud simulation | es |
dc.title | Empirical characterization and modeling of power consumption and energy aware scheduling in data centers | es |
dc.type | Tesis de maestría | es |
dc.contributor.filiacion | Muraña Jonathan, Universidad de la República (Uruguay). Facultad de Ingeniería. | - |
thesis.degree.grantor | Universidad de la República (Uruguay). Facultad de Ingeniería | es |
thesis.degree.name | Magíster en Informática | es |
dc.rights.licence | Licencia Creative Commons Atribución - No Comercial - Sin Derivadas (CC - By-NC-ND 4.0) | es |
Aparece en las colecciones: | Tesis de posgrado - Instituto de Computación |
Ficheros en este ítem:
Fichero | Descripción | Tamaño | Formato | ||
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MUR19.pdf | Tesis de maestría | 839,33 kB | Adobe PDF | Visualizar/Abrir |
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