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Please use this identifier to cite or link to this item: http://hdl.handle.net/1843/43019
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dc.contributor.advisor1Daniel Fernandes Macedopt_BR
dc.contributor.advisor1Latteshttp://lattes.cnpq.br/8758395845049687pt_BR
dc.contributor.advisor-co1José Marcos Silva Nogueirapt_BR
dc.contributor.advisor-co2Magnos Martinellopt_BR
dc.creatorMarcos Magno de Carvalhopt_BR
dc.creator.Latteshttp://lattes.cnpq.br/2187821491610637pt_BR
dc.date.accessioned2022-07-07T14:10:38Z-
dc.date.available2022-07-07T14:10:38Z-
dc.date.issued2021-10-01-
dc.identifier.urihttp://hdl.handle.net/1843/43019-
dc.description.resumoCloud computing has been successful in providing computing resources to deploy highly available applications for multiple content providers (cloud customers). In this case, to improve resource usage, the cloud provider tends to share its computing resources between different customers, co-locating applications on the same server. However, co-located applications generate interference with each other, which can cause degradation of the applications. Furthermore, each application demands a different type of resource and performance, which makes resource management even more complex. To mitigate this, the container scheduling process uses metrics based on Quality of Service (QoS), which are pre-established and specified in the Service Level Objectives (SLO). However, for applications where users' experience is important and measurable, QoS-based SLO is insufficient to guarantee end-users good Quality of Experience (QoE). This is because the QoS metrics do not correctly reflect the users' experience.The proposal of this dissertation deals with this problem, proposing a QoE-aware container scheduler/rescheduler in an environment where applications are co-located. To that end, we propose a new approach that considers cloud metrics to estimate the QoE that the cloud can offer. Furthermore, we propose using QoE as a performance metric in SLO and an algorithm that uses QoE estimation to perform the container scheduling/rescheduling. Finally, we carried out an experimental evaluation of our proposal considering two different streaming video applications. The results obtained show that QoE-aware scheduling can increase users' QoE, in addition to improving other QoE factors, such as stall event and resolution change. Furthermore, our results showed that our scheduler/reschedule was able to reduce the amount of resources used.pt_BR
dc.description.sponsorshipCNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológicopt_BR
dc.languageengpt_BR
dc.publisherUniversidade Federal de Minas Geraispt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.programPrograma de Pós-Graduação em Ciência da Computaçãopt_BR
dc.publisher.initialsUFMGpt_BR
dc.rightsAcesso Abertopt_BR
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/pt/*
dc.subjectCloud Computingpt_BR
dc.subjectContainer Schedulerpt_BR
dc.subjectDeep Learningpt_BR
dc.subjectVideo Streamingpt_BR
dc.subject.otherComputação – Tesespt_BR
dc.subject.otherComputação em nuvem – Tesespt_BR
dc.subject.otherAprendizado profundo – Tesespt_BR
dc.subject.otherTransmissão de vídeo – Tesespt_BR
dc.titleQoE-aware container scheduling for co-located cloud applicationspt_BR
dc.title.alternativeAgendamento de contêiner ciente da QoE para aplicações em nuvem co-localizadospt_BR
dc.typeDissertaçãopt_BR
Appears in Collections:Dissertações de Mestrado

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