Person:
Olcoz Herrero, Katzalin

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First Name
Katzalin
Last Name
Olcoz Herrero
URI
https://hdl.handle.net/20.500.14352/74976
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Ciencias Físicas
Department
Arquitectura de Computadores y Automática
Area
Arquitectura y Tecnología de Computadores
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Author: Risco Martín, José Luis × Subject: 004 ×

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    Server Power Modeling for Run-time Energy Optimization of Cloud Computing Facilities.
    (Energy Procedia, 6th International conference on sustainability in energy and buildings, 2014) Arroba, Patricia; Risco Martín, José Luis; Zapater Sancho, Marina; Moya, José Manuel; Ayala Rodrigo, José Luis; Olcoz Herrero, Katzalin
    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 simultaneously 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.