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A methodology for full-system power modeling in heterogeneous data centers
dc.contributor.author | Canuto, Mauro |
dc.contributor.author | Bosch, Raimon |
dc.contributor.author | Macías Lloret, Mario |
dc.contributor.author | Guitart Fernández, Jordi |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors |
dc.contributor.other | Barcelona Supercomputing Center |
dc.date.accessioned | 2017-01-27T09:42:49Z |
dc.date.available | 2017-01-27T09:42:49Z |
dc.date.issued | 2016 |
dc.identifier.citation | Canuto, M., Bosch, R., Macías, M., Guitart, J. A methodology for full-system power modeling in heterogeneous data centers. A: IEEE/ACM International Conference on Utility and Cloud Computing. "Proceedings of the 9th IEEE/ACM International Conference on Utility and Cloud Computing (UCC'16)". Shanghai: Association for Computing Machinery (ACM), 2016, p. 20-29. |
dc.identifier.isbn | 978-1-4503-4616-0 |
dc.identifier.uri | http://hdl.handle.net/2117/100178 |
dc.description.abstract | The need for energy-awareness in current data centers has encouraged the use of power modeling to estimate their power consumption. However, existing models present noticeable limitations, which make them application-dependent, platform-dependent, inaccurate, or computationally complex. In this paper, we propose a platform-and application-agnostic methodology for full-system power modeling in heterogeneous data centers that overcomes those limitations. It derives a single model per platform, which works with high accuracy for heterogeneous applications with different patterns of resource usage and energy consumption, by systematically selecting a minimum set of resource usage indicators and extracting complex relations among them that capture the impact on energy consumption of all the resources in the system. We demonstrate our methodology by generating power models for heterogeneous platforms with very different power consumption profiles. Our validation experiments with real Cloud applications show that such models provide high accuracy (around 5% of average estimation error). |
dc.description.sponsorship | This work is supported by the Spanish Ministry of Economy and Competitiveness under contract TIN2015-65316-P, by the Gener- alitat de Catalunya under contract 2014-SGR-1051, and by the European Commission under FP7-SMARTCITIES-2013 contract 608679 (RenewIT) and FP7-ICT-2013-10 contracts 610874 (AS- CETiC) and 610456 (EuroServer). |
dc.format.extent | 10 p. |
dc.language.iso | eng |
dc.publisher | Association for Computing Machinery (ACM) |
dc.subject | Àrees temàtiques de la UPC::Informàtica::Arquitectura de computadors |
dc.subject.lcsh | Data processing service centers -- Energy consumption |
dc.subject.other | Cloud applications |
dc.subject.other | Energy awareness |
dc.subject.other | Estimation errors |
dc.subject.other | Heterogeneous data centers |
dc.subject.other | Heterogeneous platforms |
dc.subject.other | High-accuracy |
dc.subject.other | Resource usage |
dc.subject.other | Single models |
dc.title | A methodology for full-system power modeling in heterogeneous data centers |
dc.type | Conference lecture |
dc.subject.lemac | Centres informàtics -- Consum d'energia |
dc.contributor.group | Universitat Politècnica de Catalunya. CAP - Grup de Computació d'Altes Prestacions |
dc.identifier.doi | 10.1145/2996890.2996899 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | http://dl.acm.org/citation.cfm?id=2996899&CFID=708314705&CFTOKEN=96079792 |
dc.rights.access | Open Access |
local.identifier.drac | 19374593 |
dc.description.version | Postprint (author's final draft) |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO//TIN2015-65316-P/ES/COMPUTACION DE ALTAS PRESTACIONES VII/ |
local.citation.author | Canuto, M.; Bosch, R.; Macías, M.; Guitart, J. |
local.citation.contributor | IEEE/ACM International Conference on Utility and Cloud Computing |
local.citation.pubplace | Shanghai |
local.citation.publicationName | Proceedings of the 9th IEEE/ACM International Conference on Utility and Cloud Computing (UCC'16) |
local.citation.startingPage | 20 |
local.citation.endingPage | 29 |