Measuring stream processing systems adaptability under dynamic workloads

[EN] Data streaming belongs to the Big Data ecosystem, which generates high-frequency data streams featuring time-varying characteristics that challenge the traditional stream processing systems capacities. To deal with this problem, many self-adaptive stream processing systems have been proposed. D...

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Detalles Bibliográficos
Autores: Hidalgo, Nicolas, Vasquez, Cristobal, Wladdimiro, Daniel, Rosas-Olivos, Erika Susana
Tipo de recurso: artículo
Fecha de publicación:2018
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:10251/232890
Acceso en línea:https://riunet.upv.es/handle/10251/232890
Access Level:acceso abierto
Palabra clave:Adaptation index
Benchmarks
Autonomic systems
Stream processing
09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación
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spelling Measuring stream processing systems adaptability under dynamic workloadsHidalgo, NicolasVasquez, CristobalWladdimiro, DanielRosas-Olivos, Erika SusanaAdaptation indexBenchmarksAutonomic systemsStream processing09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación[EN] Data streaming belongs to the Big Data ecosystem, which generates high-frequency data streams featuring time-varying characteristics that challenge the traditional stream processing systems capacities. To deal with this problem, many self-adaptive stream processing systems have been proposed. Despite the evolution of self-adaptive systems, there is still a lack of standardized benchmarking systems to enable scientists to evaluate the autonomic capacities of their solutions. In this work, we propose an index called AI-SPS inspired by the human cerebral auto-regulation process. The index quantifies the capacity of an adaptive stream processing systems to self-adapt in the presence of highly dynamic workloads. An index of this nature will help the scientific community generate fair comparisons among literature with the aim of creating better solutions. We validate our proposal by evaluating the adaptive behavior of two state of the art self-adaptive stream processing systems. Tests were performed using real traffic datasets adapted specifically to stress the processing system. Results show that the proposed index quantifies the adaptation capacity of self-adaptive stream processing systems effectively.ElsevierDepartamento de Informática de Sistemas y ComputadoresGrupo de Redes de ComputadoresRepositorio Institucional de la Universitat Politècnica de València Riunet20182018-11-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://riunet.upv.es/handle/10251/232890reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2328902026-06-13T07:49:27Z
dc.title.none.fl_str_mv Measuring stream processing systems adaptability under dynamic workloads
title Measuring stream processing systems adaptability under dynamic workloads
spellingShingle Measuring stream processing systems adaptability under dynamic workloads
Hidalgo, Nicolas
Adaptation index
Benchmarks
Autonomic systems
Stream processing
09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación
title_short Measuring stream processing systems adaptability under dynamic workloads
title_full Measuring stream processing systems adaptability under dynamic workloads
title_fullStr Measuring stream processing systems adaptability under dynamic workloads
title_full_unstemmed Measuring stream processing systems adaptability under dynamic workloads
title_sort Measuring stream processing systems adaptability under dynamic workloads
dc.creator.none.fl_str_mv Hidalgo, Nicolas
Vasquez, Cristobal
Wladdimiro, Daniel
Rosas-Olivos, Erika Susana
author Hidalgo, Nicolas
author_facet Hidalgo, Nicolas
Vasquez, Cristobal
Wladdimiro, Daniel
Rosas-Olivos, Erika Susana
author_role author
author2 Vasquez, Cristobal
Wladdimiro, Daniel
Rosas-Olivos, Erika Susana
author2_role author
author
author
dc.contributor.none.fl_str_mv Departamento de Informática de Sistemas y Computadores
Grupo de Redes de Computadores
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Adaptation index
Benchmarks
Autonomic systems
Stream processing
09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación
topic Adaptation index
Benchmarks
Autonomic systems
Stream processing
09.- Desarrollar infraestructuras resilientes, promover la industrialización inclusiva y sostenible, y fomentar la innovación
description [EN] Data streaming belongs to the Big Data ecosystem, which generates high-frequency data streams featuring time-varying characteristics that challenge the traditional stream processing systems capacities. To deal with this problem, many self-adaptive stream processing systems have been proposed. Despite the evolution of self-adaptive systems, there is still a lack of standardized benchmarking systems to enable scientists to evaluate the autonomic capacities of their solutions. In this work, we propose an index called AI-SPS inspired by the human cerebral auto-regulation process. The index quantifies the capacity of an adaptive stream processing systems to self-adapt in the presence of highly dynamic workloads. An index of this nature will help the scientific community generate fair comparisons among literature with the aim of creating better solutions. We validate our proposal by evaluating the adaptive behavior of two state of the art self-adaptive stream processing systems. Tests were performed using real traffic datasets adapted specifically to stress the processing system. Results show that the proposed index quantifies the adaptation capacity of self-adaptive stream processing systems effectively.
publishDate 2018
dc.date.none.fl_str_mv 2018
2018-11-01
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/10251/232890
url https://riunet.upv.es/handle/10251/232890
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/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 - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
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
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score 15.811543

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