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

Arroba García, Patricia and Risco Martín, José Luis and Zapater Sancho, Marina and Moya Fernández, José Manuel and Ayala Rodrigo, José Luis and 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.

Description

Title: Server power modeling for run-time energy optimization of cloud computing facilities
Author/s:
  • Arroba García, Patricia
  • Risco Martín, José Luis
  • Zapater Sancho, Marina
  • Moya Fernández, José Manuel
  • Ayala Rodrigo, José Luis
  • Olcoz, Katzalin
Item Type: Article
Título de Revista/Publicación: Energy Procedia
Date: 2014
ISSN: 1876-6102
Volume: 62
Subjects:
Freetext Keywords: Green Data Centers, Sustainable Cloud Computing, Power modeling, Particle Swarm Optimization
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Ingeniería Electrónica
Creative Commons Licenses: Recognition - No derivative works - Non commercial

Full text

[img]
Preview
PDF - Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (431kB) | Preview

Abstract

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.

More information

Item ID: 36177
DC Identifier: http://oa.upm.es/36177/
OAI Identifier: oai:oa.upm.es:36177
DOI: 10.1016/j.egypro.2014.12.402
Official URL: http://www.sciencedirect.com/science/article/pii/S187661021403433X
Deposited by: Memoria Investigacion
Deposited on: 01 Jul 2015 18:56
Last Modified: 01 Jul 2015 18:56
  • Logo InvestigaM (UPM)
  • Logo GEOUP4
  • Logo Open Access
  • Open Access
  • Logo Sherpa/Romeo
    Check whether the anglo-saxon journal in which you have published an article allows you to also publish it under open access.
  • Logo Dulcinea
    Check whether the spanish journal in which you have published an article allows you to also publish it under open access.
  • Logo de Recolecta
  • Logo del Observatorio I+D+i UPM
  • Logo de OpenCourseWare UPM