Server power modeling for run-time energy optimization of cloud computing facilities

Arroba García, Patricia; Risco Martín, José Luis; Zapater Sancho, Marina; Moya Fernández, José Manuel; Ayala Rodrigo, José Luis y Olcoz, Katzalin (2014). Server power modeling for run-time energy optimization of cloud computing facilities. "Energy Procedia", v. 62 ; pp. 401-410. ISSN 1876-6102. https://doi.org/10.1016/j.egypro.2014.12.402.

Descripción

Título: Server power modeling for run-time energy optimization of cloud computing facilities
Autor/es:
  • Arroba García, Patricia
  • Risco Martín, José Luis
  • Zapater Sancho, Marina
  • Moya Fernández, José Manuel
  • Ayala Rodrigo, José Luis
  • Olcoz, Katzalin
Tipo de Documento: Artículo
Título de Revista/Publicación: Energy Procedia
Fecha: 2014
Volumen: 62
Materias:
Palabras Clave Informales: Green Data Centers, Sustainable Cloud Computing, Power modeling, Particle Swarm Optimization
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Ingeniería Electrónica
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

As advanced Cloud services are becoming mainstream, the contribution of data centers in the overall power consumption of modern cities is growing dramatically. The average consumption of a single data center is equivalent to the energy consumption of 25.000 households. Modeling the power consumption for these infrastructures is crucial to anticipate the effects of aggressive optimization policies, but accurate and fast power modeling is a complex challenge for high-end servers not yet satisfied by analytical approaches. This work proposes an automatic method, based on Multi-Objective Particle Swarm Optimization, for the identification of power models of enterprise servers in Cloud data centers. Our approach, as opposed to previous procedures, does not only consider the workload consolidation for deriving the power model, but also incorporates other non traditional factors like the static power consumption and its dependence with temperature. Our experimental results shows that we reach slightly better models than classical approaches, but simul- taneously simplifying the power model structure and thus the numbers of sensors needed, which is very promising for a short-term energy prediction. This work, validated with real Cloud applications, broadens the possibilities to derive efficient energy saving techniques for Cloud facilities.

Más información

ID de Registro: 36177
Identificador DC: http://oa.upm.es/36177/
Identificador OAI: oai:oa.upm.es:36177
Identificador DOI: 10.1016/j.egypro.2014.12.402
URL Oficial: http://www.sciencedirect.com/science/article/pii/S187661021403433X
Depositado por: Memoria Investigacion
Depositado el: 01 Jul 2015 18:56
Ultima Modificación: 01 Jul 2015 18:56
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