QoE-aware container scheduling for co-located cloud applications

Detalhes bibliográficos
Autor(a) principal: Marcos Magno de Carvalho
Data de Publicação: 2021
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Institucional da UFMG
Texto Completo: http://hdl.handle.net/1843/43019
Resumo: CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico
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spelling Daniel Fernandes Macedohttp://lattes.cnpq.br/8758395845049687José Marcos Silva NogueiraMagnos Martinellohttp://lattes.cnpq.br/2187821491610637Marcos Magno de Carvalho2022-07-07T14:10:38Z2022-07-07T14:10:38Z2021-10-01http://hdl.handle.net/1843/43019CNPq - Conselho Nacional de Desenvolvimento Científico e TecnológicoCloud 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.engUniversidade Federal de Minas GeraisPrograma de Pós-Graduação em Ciência da ComputaçãoUFMGBrasilhttp://creativecommons.org/licenses/by-nc-nd/3.0/pt/info:eu-repo/semantics/openAccessComputação – TesesComputação em nuvem – TesesAprendizado profundo – TesesTransmissão de vídeo – TesesCloud ComputingContainer SchedulerDeep LearningVideo StreamingQoE-aware container scheduling for co-located cloud applicationsAgendamento de contêiner ciente da QoE para aplicações em nuvem co-localizadosinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisreponame:Repositório Institucional da UFMGinstname:Universidade Federal de Minas Gerais (UFMG)instacron:UFMGORIGINALversão _final_072022.pdfversão _final_072022.pdfapplication/pdf74622963https://repositorio.ufmg.br/bitstream/1843/43019/6/vers%c3%a3o%20_final_072022.pdfaace4046e69a7b6730a17d385c767e16MD56LICENSElicense.txtlicense.txttext/plain; charset=utf-82118https://repositorio.ufmg.br/bitstream/1843/43019/7/license.txtcda590c95a0b51b4d15f60c9642ca272MD57CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8811https://repositorio.ufmg.br/bitstream/1843/43019/2/license_rdfcfd6801dba008cb6adbd9838b81582abMD521843/430192022-07-07 11:10:39.151oai:repositorio.ufmg.br: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ório de PublicaçõesPUBhttps://repositorio.ufmg.br/oaiopendoar:2022-07-07T14:10:39Repositório Institucional da UFMG - Universidade Federal de Minas Gerais (UFMG)false
dc.title.pt_BR.fl_str_mv QoE-aware container scheduling for co-located cloud applications
dc.title.alternative.pt_BR.fl_str_mv Agendamento de contêiner ciente da QoE para aplicações em nuvem co-localizados
title QoE-aware container scheduling for co-located cloud applications
spellingShingle QoE-aware container scheduling for co-located cloud applications
Marcos Magno de Carvalho
Cloud Computing
Container Scheduler
Deep Learning
Video Streaming
Computação – Teses
Computação em nuvem – Teses
Aprendizado profundo – Teses
Transmissão de vídeo – Teses
title_short QoE-aware container scheduling for co-located cloud applications
title_full QoE-aware container scheduling for co-located cloud applications
title_fullStr QoE-aware container scheduling for co-located cloud applications
title_full_unstemmed QoE-aware container scheduling for co-located cloud applications
title_sort QoE-aware container scheduling for co-located cloud applications
author Marcos Magno de Carvalho
author_facet Marcos Magno de Carvalho
author_role author
dc.contributor.advisor1.fl_str_mv Daniel Fernandes Macedo
dc.contributor.advisor1Lattes.fl_str_mv http://lattes.cnpq.br/8758395845049687
dc.contributor.advisor-co1.fl_str_mv José Marcos Silva Nogueira
dc.contributor.advisor-co2.fl_str_mv Magnos Martinello
dc.contributor.authorLattes.fl_str_mv http://lattes.cnpq.br/2187821491610637
dc.contributor.author.fl_str_mv Marcos Magno de Carvalho
contributor_str_mv Daniel Fernandes Macedo
José Marcos Silva Nogueira
Magnos Martinello
dc.subject.por.fl_str_mv Cloud Computing
Container Scheduler
Deep Learning
Video Streaming
topic Cloud Computing
Container Scheduler
Deep Learning
Video Streaming
Computação – Teses
Computação em nuvem – Teses
Aprendizado profundo – Teses
Transmissão de vídeo – Teses
dc.subject.other.pt_BR.fl_str_mv Computação – Teses
Computação em nuvem – Teses
Aprendizado profundo – Teses
Transmissão de vídeo – Teses
description CNPq - Conselho Nacional de Desenvolvimento Científico e Tecnológico
publishDate 2021
dc.date.issued.fl_str_mv 2021-10-01
dc.date.accessioned.fl_str_mv 2022-07-07T14:10:38Z
dc.date.available.fl_str_mv 2022-07-07T14:10:38Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/1843/43019
url http://hdl.handle.net/1843/43019
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/3.0/pt/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/3.0/pt/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Universidade Federal de Minas Gerais
dc.publisher.program.fl_str_mv Programa de Pós-Graduação em Ciência da Computação
dc.publisher.initials.fl_str_mv UFMG
dc.publisher.country.fl_str_mv Brasil
publisher.none.fl_str_mv Universidade Federal de Minas Gerais
dc.source.none.fl_str_mv reponame:Repositório Institucional da UFMG
instname:Universidade Federal de Minas Gerais (UFMG)
instacron:UFMG
instname_str Universidade Federal de Minas Gerais (UFMG)
instacron_str UFMG
institution UFMG
reponame_str Repositório Institucional da UFMG
collection Repositório Institucional da UFMG
bitstream.url.fl_str_mv https://repositorio.ufmg.br/bitstream/1843/43019/6/vers%c3%a3o%20_final_072022.pdf
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