Using grammatical evolution techniques to model the dynamic power consumption of enterprise servers

Salinas Hilburg, Juan Carlos and Zapater Sancho, Marina and Risco Martín, José Luis and Moya Fernández, José Manuel and Ayala Rodrigo, José Luis (2015). Using grammatical evolution techniques to model the dynamic power consumption of enterprise servers. In: "Ninth International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS 2015)", 08/07/2015 - 10/07/2015, Blumenau, Brasil. pp. 110-117. https://doi.org/10.1109/CISIS.2015.16.

Description

Title: Using grammatical evolution techniques to model the dynamic power consumption of enterprise servers
Author/s:
  • Salinas Hilburg, Juan Carlos
  • Zapater Sancho, Marina
  • Risco Martín, José Luis
  • Moya Fernández, José Manuel
  • Ayala Rodrigo, José Luis
Item Type: Presentation at Congress or Conference (Article)
Event Title: Ninth International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS 2015)
Event Dates: 08/07/2015 - 10/07/2015
Event Location: Blumenau, Brasil
Title of Book: Ninth International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS 2015)
Date: 2015
Subjects:
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Ingeniería Electrónica
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

The increasing demand for computational resources has led to a significant growth of data center facilities. A major concern has appeared regarding energy efficiency and consumption in servers and data centers. The use of flexible and scalable server power models is a must in order to enable proactive energy optimization strategies. This paper proposes the use of Evolutionary Computation to obtain a model for server dynamic power consumption. To accomplish this, we collect a significant number of server performance counters for a wide range of sequential and parallel applications, and obtain a model via Genetic Programming techniques. Our methodology enables the unsupervised generation of models for arbitrary server architectures, in a way that is robust to the type of application being executed in the server. With our generated models, we are able to predict the overall server power consumption for arbitrary workloads, outperforming previous approaches in the state-of-the-art.

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainIPT-2012-1041-430000UnspecifiedUnspecifiedGestión óptima de modos de bajo consumo en cloud computing. Acrónimo
Government of SpainRTC-2014-2717-3UnspecifiedUnspecifiedOPTIMIZACIÓN ENERGÉTICA DE CENTROS DE DATOS DE INFRAESTRUCTURAS CLOUD BASADAS EN OPENSTACK
Government of SpainTEC-2012-33892UnspecifiedUnspecifiedTECNOLOGIAS HW/SW PARA LA EFICIENCIA ENERGETICA EN SISTEMAS DE COMPUTACION DISTRIBUIDOS

More information

Item ID: 42753
DC Identifier: http://oa.upm.es/42753/
OAI Identifier: oai:oa.upm.es:42753
DOI: 10.1109/CISIS.2015.16
Deposited by: Memoria Investigacion
Deposited on: 16 Jul 2016 09:34
Last Modified: 16 Jul 2016 09:34
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