Cloud White: Detecting and Estimating QoS Degradation of Latency-Critical Workloads in the Public Cloud

[EN] The increasing popularity of cloud computing has forced cloud providers to build economies of scale to meet the growing demand. Nowadays, data-centers include thousands of physical machines, each hosting many virtual machines (VMs), which share the main system resources, causing interference th...

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Detalles Bibliográficos
Autores: Pons-Escat, Lucía|||0000-0002-4582-7744, Feliu-Pérez, Josué|||0000-0003-3017-4266, Sahuquillo Borrás, Julio|||0000-0001-8630-4846, Gómez Requena, María Engracia|||0000-0003-1466-4118, Petit Martí, Salvador Vicente|||0000-0003-2426-4134, Pons Terol, Julio|||0000-0002-5654-6753, Huang, Chaoyi
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/200990
Acceso en línea:https://riunet.upv.es/handle/10251/200990
Access Level:acceso abierto
Palabra clave:Cloud computing
Public cloud
Virtualization
Interference
Performance estimation
QoS
Tail latency
Latency-critical workloads
ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES
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network_acronym_str ES
network_name_str España
repository_id_str
spelling Cloud White: Detecting and Estimating QoS Degradation of Latency-Critical Workloads in the Public CloudPons-Escat, Lucía|||0000-0002-4582-7744Feliu-Pérez, Josué|||0000-0003-3017-4266Sahuquillo Borrás, Julio|||0000-0001-8630-4846Gómez Requena, María Engracia|||0000-0003-1466-4118Petit Martí, Salvador Vicente|||0000-0003-2426-4134Pons Terol, Julio|||0000-0002-5654-6753Huang, ChaoyiCloud computingPublic cloudVirtualizationInterferencePerformance estimationQoSTail latencyLatency-critical workloadsARQUITECTURA Y TECNOLOGIA DE COMPUTADORES[EN] The increasing popularity of cloud computing has forced cloud providers to build economies of scale to meet the growing demand. Nowadays, data-centers include thousands of physical machines, each hosting many virtual machines (VMs), which share the main system resources, causing interference that can significantly impact on performance. Frequently, these data-centers run latency-critical workloads, whose performance is determined by tail latency, which is very sensitive to the interference of co-running workloads. To prevent QoS violations, cloud providers adopt overprovisioning strategies but they reduce the server utilization and increase the costs. A mechanism that accurately estimates performance degradation dynamically in a production system would allow cloud providers to improve the servers' utilization. In this work we propose Cloud White, an approach that is able to detect the inter-VM interference in scenarios with multiple co-located latency-critical VMs and estimate the performance degradation using multi-variable regression models. Unlike previous proposals, Cloud White is built taking into account the limitations of a public cloud production system. Experimental results show that Cloud White is able to estimate performance degradation with a small overall prediction error of 5%.This work has been supported by Huawei Cloud, and in part by Spanish Ministerio de Universidades under grant FPU18/01948, and by Spanish Ministerio de Universidades and European ERDF under grants RTI2018-098156-B-C51 and PID2021-123627OB-C51. Funding for open access charge: CRUE-Universitat Politec-nica de Valencia.ElsevierDepartamento de Informática de Sistemas y ComputadoresEscuela Técnica Superior de Ingeniería InformáticaGrupo de Arquitecturas ParalelasAGENCIA ESTATAL DE INVESTIGACIONEuropean Regional Development FundMINISTERIO DE CIENCIA E INNOVACIONUniversitat Politècnica de ValènciaRepositorio Institucional de la Universitat Politècnica de València Riunet20232023-01-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/200990reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 RTI2018-098156-B-C51 TECNOLOGIAS INNOVADORAS DE PROCESADORES, ACELERADORES Y REDES, PARA CENTROS DE DATOS Y COMPUTACION DE ALTAS PRESTACIONESAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2021-123627OB-C51 MEJORA DEL PROCESADOR, SUBSISTEMA DE MEMORIA, ACELERADORES Y REDESMinisterio de Universidades MIU FPU18%2F01948 GESTION EFICIENTE DE RECURSOS COMPARTIDOS EN HIGH-PERFORMANCE COMPUTING Y CLOUD COMPUTINGopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2009902026-06-13T07:49:27Z
dc.title.none.fl_str_mv Cloud White: Detecting and Estimating QoS Degradation of Latency-Critical Workloads in the Public Cloud
title Cloud White: Detecting and Estimating QoS Degradation of Latency-Critical Workloads in the Public Cloud
spellingShingle Cloud White: Detecting and Estimating QoS Degradation of Latency-Critical Workloads in the Public Cloud
Pons-Escat, Lucía|||0000-0002-4582-7744
Cloud computing
Public cloud
Virtualization
Interference
Performance estimation
QoS
Tail latency
Latency-critical workloads
ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES
title_short Cloud White: Detecting and Estimating QoS Degradation of Latency-Critical Workloads in the Public Cloud
title_full Cloud White: Detecting and Estimating QoS Degradation of Latency-Critical Workloads in the Public Cloud
title_fullStr Cloud White: Detecting and Estimating QoS Degradation of Latency-Critical Workloads in the Public Cloud
title_full_unstemmed Cloud White: Detecting and Estimating QoS Degradation of Latency-Critical Workloads in the Public Cloud
title_sort Cloud White: Detecting and Estimating QoS Degradation of Latency-Critical Workloads in the Public Cloud
dc.creator.none.fl_str_mv Pons-Escat, Lucía|||0000-0002-4582-7744
Feliu-Pérez, Josué|||0000-0003-3017-4266
Sahuquillo Borrás, Julio|||0000-0001-8630-4846
Gómez Requena, María Engracia|||0000-0003-1466-4118
Petit Martí, Salvador Vicente|||0000-0003-2426-4134
Pons Terol, Julio|||0000-0002-5654-6753
Huang, Chaoyi
author Pons-Escat, Lucía|||0000-0002-4582-7744
author_facet Pons-Escat, Lucía|||0000-0002-4582-7744
Feliu-Pérez, Josué|||0000-0003-3017-4266
Sahuquillo Borrás, Julio|||0000-0001-8630-4846
Gómez Requena, María Engracia|||0000-0003-1466-4118
Petit Martí, Salvador Vicente|||0000-0003-2426-4134
Pons Terol, Julio|||0000-0002-5654-6753
Huang, Chaoyi
author_role author
author2 Feliu-Pérez, Josué|||0000-0003-3017-4266
Sahuquillo Borrás, Julio|||0000-0001-8630-4846
Gómez Requena, María Engracia|||0000-0003-1466-4118
Petit Martí, Salvador Vicente|||0000-0003-2426-4134
Pons Terol, Julio|||0000-0002-5654-6753
Huang, Chaoyi
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Departamento de Informática de Sistemas y Computadores
Escuela Técnica Superior de Ingeniería Informática
Grupo de Arquitecturas Paralelas
AGENCIA ESTATAL DE INVESTIGACION
European Regional Development Fund
MINISTERIO DE CIENCIA E INNOVACION
Universitat Politècnica de València
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Cloud computing
Public cloud
Virtualization
Interference
Performance estimation
QoS
Tail latency
Latency-critical workloads
ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES
topic Cloud computing
Public cloud
Virtualization
Interference
Performance estimation
QoS
Tail latency
Latency-critical workloads
ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES
description [EN] The increasing popularity of cloud computing has forced cloud providers to build economies of scale to meet the growing demand. Nowadays, data-centers include thousands of physical machines, each hosting many virtual machines (VMs), which share the main system resources, causing interference that can significantly impact on performance. Frequently, these data-centers run latency-critical workloads, whose performance is determined by tail latency, which is very sensitive to the interference of co-running workloads. To prevent QoS violations, cloud providers adopt overprovisioning strategies but they reduce the server utilization and increase the costs. A mechanism that accurately estimates performance degradation dynamically in a production system would allow cloud providers to improve the servers' utilization. In this work we propose Cloud White, an approach that is able to detect the inter-VM interference in scenarios with multiple co-located latency-critical VMs and estimate the performance degradation using multi-variable regression models. Unlike previous proposals, Cloud White is built taking into account the limitations of a public cloud production system. Experimental results show that Cloud White is able to estimate performance degradation with a small overall prediction error of 5%.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/200990
url https://riunet.upv.es/handle/10251/200990
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 RTI2018-098156-B-C51 TECNOLOGIAS INNOVADORAS DE PROCESADORES, ACELERADORES Y REDES, PARA CENTROS DE DATOS Y COMPUTACION DE ALTAS PRESTACIONES
Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2021-123627OB-C51 MEJORA DEL PROCESADOR, SUBSISTEMA DE MEMORIA, ACELERADORES Y REDES
Ministerio de Universidades MIU FPU18%2F01948 GESTION EFICIENTE DE RECURSOS COMPARTIDOS EN HIGH-PERFORMANCE COMPUTING Y CLOUD COMPUTING
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869417221465309184
score 15,300724

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