Performance Evaluation of Multiple Cloud Data Centers Allocations for HPC

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dc.contributor	Barcelona Supercomputing Center
dc.contributor.author	Roloff, Eduardo
dc.contributor.author	Carreño, Emmanuell D.
dc.contributor.author	Valverde-Sanchez, Jimmy K.M.
dc.contributor.author	Diener, Matthias
dc.contributor.author	Serpa, Matheus da Silva
dc.contributor.author	Houzeaux, Guillaume
dc.contributor.author	Schnorr, Lucas M.
dc.contributor.author	Maillard, Nicolas
dc.contributor.author	Gaspary, Luciano P.
dc.contributor.author	Navaux, Philippe
dc.date	2017-04-29
dc.identifier.citation	Roloff, E. [et al.]. Performance Evaluation of Multiple Cloud Data Centers Allocations for HPC. A: 2 016 LatinAmerican High Performance Computing Conference (CARLA), México, Aug 29-Sep 02, 2016. "CARLA 2016: High Performance Computing". Springer International Publishing, 2017, p. 18-32.
dc.identifier.citation	978-3-319-57971-9
dc.identifier.citation	10.1007/978-3-319-57972-6_2
dc.identifier.uri	http://hdl.handle.net/2117/104583
dc.language.iso	eng
dc.publisher	Springer International Publishing
dc.relation	http://link.springer.com/chapter/10.1007/978-3-319-57972-6_2
dc.relation	info:eu-repo/grantAgreement/EC/H2020/689772/EU/HPC for Energy/HPC4E
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Àrees temàtiques de la UPC::Enginyeria electrònica
dc.subject	Cloud computing
dc.subject	High performance computing
dc.subject	Cloud Computing
dc.subject	HPC
dc.subject	Azure
dc.subject	MPI
dc.subject	NAS
dc.subject	Supercomputadors
dc.title	Performance Evaluation of Multiple Cloud Data Centers Allocations for HPC
dc.type	info:eu-repo/semantics/submittedVersion
dc.type	info:eu-repo/semantics/conferenceObject
dc.description.abstract	This paper evaluates the behavior of the Microsoft Azure G5 cloud instance type over multiple Data Centers. The purpose is to identify if there are major differences between them and to help the users choose the best option for their needs. Our results show that there are differences in the network level for the same instance type in different locations and inside the same location at different times. The network performance causes interference in the applications level, as we could verify in our results.
dc.description.abstract	This research received funding from the EU H2020 Programme and from MCTI/RNP-Brazil under the HPC4E project, grant agreement no. 689772. Experiments presented in this paper were carried out using the Grid'5000 testbed, supported by a scientific interest group hosted by Inria and including CNRS, RENATER and several Universities as well as other organizations (see https://www.grid5000.fr). Additional funding was provided by CAPES and Microsoft.
dc.description.abstract	Peer Reviewed

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