Modelo predictivo para la asignación elástica de recursos sobre entornos NFV/SDN basados en OpenStack

En esta investigación se implementa un modelo predictivo para la asignación elástica de recursos sobre un entorno NFV/SDN basado en herramientas de código abierto como OpenStack. Usando como referencia una arquitectura que puede implementarse en entornos de bajo costo mediante herramientas de código...

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Autores:: Caviedes Valencia, Juan Camilo

Tipo de recurso:

Fecha de publicación:: 2021

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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network_acronym_str	UNACIONAL2
network_name_str	Universidad Nacional de Colombia
repository_id_str
dc.title.spa.fl_str_mv	Modelo predictivo para la asignación elástica de recursos sobre entornos NFV/SDN basados en OpenStack
dc.title.translated.eng.fl_str_mv	Predictive Model for Elastic Resource Allocation on NFV/SDN Environments based on OpenStack
title	Modelo predictivo para la asignación elástica de recursos sobre entornos NFV/SDN basados en OpenStack
spellingShingle	Modelo predictivo para la asignación elástica de recursos sobre entornos NFV/SDN basados en OpenStack 000 - Ciencias de la computación, información y obras generales Ingeniería de software Autoescalamiento SDN HTM Infraestructura Virtual Arquitectura de Código Abierto Alta Disponibilidad Modelo Predictivo NFV Autoscaling Virtual Infrastructure Open Source Architecture High availability Predictive Model Red informática Computer networks
title_short	Modelo predictivo para la asignación elástica de recursos sobre entornos NFV/SDN basados en OpenStack
title_full	Modelo predictivo para la asignación elástica de recursos sobre entornos NFV/SDN basados en OpenStack
title_fullStr	Modelo predictivo para la asignación elástica de recursos sobre entornos NFV/SDN basados en OpenStack
title_full_unstemmed	Modelo predictivo para la asignación elástica de recursos sobre entornos NFV/SDN basados en OpenStack
title_sort	Modelo predictivo para la asignación elástica de recursos sobre entornos NFV/SDN basados en OpenStack
dc.creator.fl_str_mv	Caviedes Valencia, Juan Camilo
dc.contributor.advisor.none.fl_str_mv	Niño Vásquez, Luis Fernando Rueda Pepinosa, Diego Fernando
dc.contributor.author.none.fl_str_mv	Caviedes Valencia, Juan Camilo
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales
topic	000 - Ciencias de la computación, información y obras generales Ingeniería de software Autoescalamiento SDN HTM Infraestructura Virtual Arquitectura de Código Abierto Alta Disponibilidad Modelo Predictivo NFV Autoscaling Virtual Infrastructure Open Source Architecture High availability Predictive Model Red informática Computer networks
dc.subject.lemb.none.fl_str_mv	Ingeniería de software
dc.subject.proposal.spa.fl_str_mv	Autoescalamiento SDN HTM Infraestructura Virtual Arquitectura de Código Abierto Alta Disponibilidad Modelo Predictivo
dc.subject.proposal.none.fl_str_mv	NFV
dc.subject.proposal.eng.fl_str_mv	Autoscaling Virtual Infrastructure Open Source Architecture High availability Predictive Model
dc.subject.unesco.none.fl_str_mv	Red informática Computer networks
description	En esta investigación se implementa un modelo predictivo para la asignación elástica de recursos sobre un entorno NFV/SDN basado en herramientas de código abierto como OpenStack. Usando como referencia una arquitectura que puede implementarse en entornos de bajo costo mediante herramientas de código abierto, se adecúa una metodología de autoescalamiento basada en recomendaciones del 3GPP. Luego, utilizando el algoritmo HTM para predecir tendencias, se efectúan asignaciones proactivas de recursos según reglas de violación de umbral, definidas en un algoritmo de autoescalamiento que sintetiza la asignación elástica de recursos. Los datos que enriquecen el modelo predictivo se generan siguiendo la tendencia de la demanda de recursos de una red móvil real. Los resultados muestran que, a través del modelo propuesto, es posible reducir el tiempo entre identificar la necesidad de escalar y culminar el escalamiento, en comparación con soluciones conocidas de computación en la nube. Además, es posible mantener la disponibilidad del servicio mientras se mejora la latencia en el tiempo de conexión al mismo.
publishDate	2021
dc.date.accessioned.none.fl_str_mv	2021-06-15T17:35:27Z
dc.date.available.none.fl_str_mv	2021-06-15T17:35:27Z
dc.date.issued.none.fl_str_mv	2021-06-02
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/79636
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/79636 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
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spelling	Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Niño Vásquez, Luis Fernandobc784b82735e16fe53653c3f5c8f3bbeRueda Pepinosa, Diego Fernando4fa0efe2772477207f4358c8529b2786Caviedes Valencia, Juan Camiloe9a7242c5eceb44543cfd1dcd736fab72021-06-15T17:35:27Z2021-06-15T17:35:27Z2021-06-02https://repositorio.unal.edu.co/handle/unal/79636Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/En esta investigación se implementa un modelo predictivo para la asignación elástica de recursos sobre un entorno NFV/SDN basado en herramientas de código abierto como OpenStack. Usando como referencia una arquitectura que puede implementarse en entornos de bajo costo mediante herramientas de código abierto, se adecúa una metodología de autoescalamiento basada en recomendaciones del 3GPP. Luego, utilizando el algoritmo HTM para predecir tendencias, se efectúan asignaciones proactivas de recursos según reglas de violación de umbral, definidas en un algoritmo de autoescalamiento que sintetiza la asignación elástica de recursos. Los datos que enriquecen el modelo predictivo se generan siguiendo la tendencia de la demanda de recursos de una red móvil real. Los resultados muestran que, a través del modelo propuesto, es posible reducir el tiempo entre identificar la necesidad de escalar y culminar el escalamiento, en comparación con soluciones conocidas de computación en la nube. Además, es posible mantener la disponibilidad del servicio mientras se mejora la latencia en el tiempo de conexión al mismo.diagramas, ilustraciones a color, tablasThis research implements a predictive model for the elastic allocation of resources on an NFV/SDN environment based on open source tools such as OpenStack. Using as a reference an architecture that can be implemented in low-cost environments using open source tools, an autoscaling methodology based on 3GPP recommendations is adapted. Then, using HTM algorithm to predict trends, proactive resource allocations are made based on threshold violation rules defined in an autoscaling algorithm that synthesizes elastic resource allocation. The data that enrich the predictive model is generated following the trend of the demand for resources of a real mobile network. The results show that, through the proposed model, it is possible to reduce the time between identifying the need to scale and completing the scaling compared to known cloud computing solutions. In addition, it is possible to maintain the availability of the service while improving the latency in connection time to it.MaestríaMagíster en Ingeniería - TelecomunicacionesMetodología cuantitativa con la implementación real de sistemas de cómputo.Redes y Sistemas de Telecomunicaciones1 recurso en línea (111 páginas)application/pdfspaUniversidad Nacional de ColombiaBogotá - Ingeniería - Maestría en Ingeniería - TelecomunicacionesDepartamento de Ingeniería de Sistemas e IndustrialFacultad de IngenieríaBogotáUniversidad Nacional de Colombia - Sede Bogotá000 - Ciencias de la computación, información y obras generalesIngeniería de softwareAutoescalamientoSDNHTMInfraestructura VirtualArquitectura de Código AbiertoAlta DisponibilidadModelo PredictivoNFVAutoscalingVirtual InfrastructureOpen Source ArchitectureHigh availabilityPredictive ModelRed informáticaComputer networksModelo predictivo para la asignación elástica de recursos sobre entornos NFV/SDN basados en OpenStackPredictive Model for Elastic Resource Allocation on NFV/SDN Environments based on OpenStackTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMP. 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Turner, "OpenFlow: enabling innovation in campus networks," ACM SIGCOMM Computer Communication Review, vol. 38, no. 2, pp. 69-74, 2008.LICENSElicense.txtlicense.txttext/plain; charset=utf-83964https://repositorio.unal.edu.co/bitstream/unal/79636/1/license.txtcccfe52f796b7c63423298c2d3365fc6MD51ORIGINAL1094953263.2021.pdf1094953263.2021.pdfTesis de Maestría en Ingeniería - Telecomunicacionesapplication/pdf6322252https://repositorio.unal.edu.co/bitstream/unal/79636/2/1094953263.2021.pdfe13ef595cbec067566b09ecdd392af6fMD52CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8908https://repositorio.unal.edu.co/bitstream/unal/79636/3/license_rdf0175ea4a2d4caec4bbcc37e300941108MD53THUMBNAIL1094953263.2021.pdf.jpg1094953263.2021.pdf.jpgGenerated Thumbnailimage/jpeg4468https://repositorio.unal.edu.co/bitstream/unal/79636/4/1094953263.2021.pdf.jpgd2aeb45a1b2f05c33756c64dcb90fe98MD54unal/79636oai:repositorio.unal.edu.co:unal/796362024-07-22 00:40:18.089Repositorio Institucional 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