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dc.contributor.authorArjona Aroca, Jordi 
dc.contributor.authorFernández Anta, Antonio 
dc.date.accessioned2021-07-13T09:25:42Z
dc.date.available2021-07-13T09:25:42Z
dc.date.issued2015-04-14
dc.identifier.urihttp://hdl.handle.net/20.500.12761/76
dc.descriptionhttp://dx.doi.org/10.1109/SustainIT.2015.7101361
dc.description.abstractThe advent of cloud computing has changed the way many companies do computation, allowing them to outsource it to the cloud. This has given origin to a new kind of business, the cloud providers, which run large datacenters. In order to be competitive, cloud providers must keep the energy consumed by the datacenter low. One way to achieve this is with smart task assignment algorithms, which decide where tasks are to be placed upon their arrival. In this paper we compare the performance of multiple task assignment algorithms for saving energy. We assume tasks are in fact virtual machines that have to be assigned to physical machines, and we assume that the physical machines have a power consumption that increases superlinearly with the load. Then, we propose an assignment algorithm VMA2 and compare its performance with other state-of-the-art assignment algorithms, both theoretical or already deployed in real cloud computing platforms. VMA2 leads to low energy consumption. It outperforms the other algorithms in most situations, proving itself to be an effective assignment algorithm for cloud computing platforms.
dc.language.isoeng
dc.titleEmpirical comparison of power-efficient virtual machine assignment algorithmsen
dc.typeconference object
dc.conference.date14-15 April 2015
dc.conference.placeMadrid, Spain
dc.conference.titleThe 4th IFIP Conference on Sustainable Internet and ICT for Sustainability (SustainIT 2015)*
dc.event.typeconference
dc.pres.typepaper
dc.type.hasVersionVoR
dc.rights.accessRightsopen access
dc.page.final8
dc.page.initial1
dc.subject.keywordCloud computing
dc.subject.keywordDatacenters
dc.subject.keywordEnergy Efficiency
dc.subject.keywordLoad Balancing
dc.subject.keywordScheduling
dc.subject.keywordVirtual Machine Assignment
dc.description.refereedTRUE
dc.description.statuspub
dc.eprint.idhttp://eprints.networks.imdea.org/id/eprint/1090


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