Utilize este identificador para referenciar este registo: http://hdl.handle.net/10071/25698
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Campo DCValorIdioma
dc.contributor.authorSawicki, S.-
dc.contributor.authorBasto-Fernandes, V.-
dc.contributor.authorFrantz, R. Z.-
dc.contributor.authorRoos-Frantz, F.-
dc.contributor.editorFilipe J.,Hammoudi S.,Smialek M.,Camp O.,Filipe J.-
dc.date.accessioned2022-06-24T15:00:38Z-
dc.date.available2022-06-24T15:00:38Z-
dc.date.issued2017-
dc.identifier.isbn978-989-758-247-9-
dc.identifier.urihttp://hdl.handle.net/10071/25698-
dc.description.abstractOne of the main advances in information technology today is cloud computing. It is a great alternative for users to reduce costs related to the need to acquire and maintain computational infrastructure to develop, implement and execute software applications. Cloud computing services are offered by providers and can be classified into three main modalities: Platform-as-a-Service (PaaS), Software-as-a-Service (SaaS) and Infrastructureas-a-Service (IaaS). In IaaS, the user has a virtual machine at their disposal with the desired computational resources at a given cost. Generally, the providers offer infrastructure services divided into instances, with preestablished configurations. The main challenge faced by companies is to choose the instance that best fits their needs among the many options offered by providers. Frequently, these companies need a large computational infrastructure to manage and improve their business processes and, due to the high cost of maintaining local infrastructure, they have begun to migrate applications to the cloud in order to reduce these costs. In this paper, we introduce a proposal for price modeling of instances of virtual machines using linear regression. This approach analyzes a set of simplified hypotheses considering the following providers: Amazon EC2, Google Compute Engine and Microsoft Windows Azure.eng
dc.language.isoeng-
dc.publisherSCITEPRESS - Science and Technology Publications-
dc.rightsopenAccess-
dc.subjectCloud computingeng
dc.subjectEnterprise application integrationeng
dc.subjectIaaSeng
dc.subjectLinear regressioneng
dc.subjectPrice modelingeng
dc.titlePrice modeling of IaaS providers - An approach focused on enterprise application integrationeng
dc.typeconferenceObject-
dc.event.title19th International Conference on Enterprise Information Systems-
dc.event.typeConferênciapt
dc.event.locationPortoeng
dc.event.date2017-
dc.pagination371 - 376-
dc.peerreviewedyes-
dc.journalProceedings of the 19th International Conference on Enterprise Information Systems-
degois.publication.firstPage371-
degois.publication.lastPage376-
degois.publication.locationPortoeng
degois.publication.titlePrice modeling of IaaS providers - An approach focused on enterprise application integrationeng
dc.date.updated2022-06-24T16:00:08Z-
dc.description.versioninfo:eu-repo/semantics/acceptedVersion-
dc.identifier.doi10.5220/0006371603710376-
dc.subject.fosDomínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informáticapor
iscte.identifier.cienciahttps://ciencia.iscte-iul.pt/id/ci-pub-38576-
iscte.alternateIdentifiers.wosWOS:000697605900042-
iscte.alternateIdentifiers.scopus2-s2.0-85023181108-
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