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dc.contributor.author | Pons-Escat, Lucía | es_ES |
dc.contributor.author | Feliu-Pérez, Josué | es_ES |
dc.contributor.author | Sahuquillo Borrás, Julio | es_ES |
dc.contributor.author | Gómez Requena, María Engracia | es_ES |
dc.contributor.author | Petit Martí, Salvador Vicente | es_ES |
dc.contributor.author | Pons Terol, Julio | es_ES |
dc.contributor.author | Huang, Chaoyi | es_ES |
dc.date.accessioned | 2023-12-20T19:01:16Z | |
dc.date.available | 2023-12-20T19:01:16Z | |
dc.date.issued | 2023-01 | es_ES |
dc.identifier.issn | 0167-739X | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/200990 | |
dc.description.abstract | [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%. | es_ES |
dc.description.sponsorship | 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. | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | Elsevier | es_ES |
dc.relation.ispartof | Future Generation Computer Systems | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | Cloud computing | es_ES |
dc.subject | Public cloud | es_ES |
dc.subject | Virtualization | es_ES |
dc.subject | Interference | es_ES |
dc.subject | Performance estimation | es_ES |
dc.subject | QoS | es_ES |
dc.subject | Tail latency | es_ES |
dc.subject | Latency-critical workloads | es_ES |
dc.subject.classification | ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES | es_ES |
dc.title | Cloud White: Detecting and Estimating QoS Degradation of Latency-Critical Workloads in the Public Cloud | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.1016/j.future.2022.08.012 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-098156-B-C51/ES/TECNOLOGIAS INNOVADORAS DE PROCESADORES, ACELERADORES Y REDES, PARA CENTROS DE DATOS Y COMPUTACION DE ALTAS PRESTACIONES/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI//PID2021-123627OB-C51//TÉCNICAS INNOVADORAS PARA INFRAESTRUCTURAS, APLICACIONES Y SERVICIOS EN CENTROS DE DATOS Y SISTEMAS ALTAMENTE DISTRIBUIDOS/ | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/ //FPU18%2F01948//AYUDA PREDOCTORAL FPU-PONS ESCAT. PROYECTO: GESTION EFICIENTE DE RECURSOS COMPARTIDOS EN HIGH-PERFORMANCE COMPUTING Y CLOUD COMPUTING/ | es_ES |
dc.rights.accessRights | Abierto | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Escola Tècnica Superior d'Enginyeria Informàtica | es_ES |
dc.contributor.affiliation | Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors | es_ES |
dc.description.bibliographicCitation | Pons-Escat, L.; Feliu-Pérez, J.; Sahuquillo Borrás, J.; Gómez Requena, ME.; Petit Martí, SV.; Pons Terol, J.; Huang, C. (2023). Cloud White: Detecting and Estimating QoS Degradation of Latency-Critical Workloads in the Public Cloud. Future Generation Computer Systems. 138:13-25. https://doi.org/10.1016/j.future.2022.08.012 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.1016/j.future.2022.08.012 | es_ES |
dc.description.upvformatpinicio | 13 | es_ES |
dc.description.upvformatpfin | 25 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 138 | es_ES |
dc.relation.pasarela | S\473349 | es_ES |
dc.contributor.funder | AGENCIA ESTATAL DE INVESTIGACION | es_ES |
dc.contributor.funder | European Regional Development Fund | es_ES |
dc.contributor.funder | MINISTERIO DE CIENCIA E INNOVACION | es_ES |
dc.contributor.funder | Universitat Politècnica de València | es_ES |