Arroba, PatriciaRisco Martín, José LuisZapater Sancho, MarinaMoya, José ManuelAyala Rodrigo, José LuisOlcoz Herrero, Katzalin2023-06-192023-06-1920141876-610210.1016/j.egypro.2014.12.402https://hdl.handle.net/20.500.14352/34837© 2014 The Authors. © 2014 Elsevier Science BV. International conference on sustainability in energy and buildings (6th. 2014. Cardiff, Gales). Research by Marina Zapater has been partly supported by a PICATA predoctoral fellowship of the Moncloa Campus of International Excellence (UCM-UPM). This work has been partially supported by the Spanish Ministry of Economy and Competitiveness, under contracts TIN2008-00508, TEC2012-33892 and IPT-2012-1041-430000, and INCOTEC. The authors thankfully acknowledge the computer resources, technical expertise and assistance provided by the Centro de Supercomputacion y Visuahzacion de Madrid (CeSViMa).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.engjournal articlehttp://dx.doi.org/10.1016/j.egypro.2014.12.402http://oa.upm.es/36177/1/INVE_MEM_2014_198652.pdfopen access004Combinatorial optimizationMetaheuristicsProgramación de ordenadores (Informática)1203.23 Lenguajes de Programación