Multitree-multiobjective multicast routing for traffic engineering

Autores
Prieto, Joel; Barán, Benjamín; Crichigno, Jorge
Año de publicación
2006
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
This paper presents a new traffic engineering multitreemultiobjective multicast routing algorithm (M-MMA) that solves for the first time the GMM model for Dynamic Multicast Groups. Multitree traffic engineering uses several trees to transmit a multicast demand from a source to a set of destinations in order to balance traffic load, improving network resource utilization. Experimental results obtained by simulations using eight real network topologies show that this new approach gets trade off solutions while simultaneously considering five objective functions. As expected, when M-MMA is compared to an equivalent singletree alternative, it accommodates more traffic demand in a high traffic saturated network.
IFIP International Conference on Artificial Intelligence in Theory and Practice - Evolutionary Computation
Red de Universidades con Carreras en Informática (RedUNCI)
Materia
Ciencias Informáticas
traffic engineering
routing algorithm
M-MMA
GMM model
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/23915
Acceder
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spelling Multitree-multiobjective multicast routing for traffic engineeringPrieto, JoelBarán, BenjamínCrichigno, JorgeCiencias Informáticastraffic engineeringrouting algorithmM-MMAGMM modelThis paper presents a new traffic engineering multitreemultiobjective multicast routing algorithm (M-MMA) that solves for the first time the GMM model for Dynamic Multicast Groups. Multitree traffic engineering uses several trees to transmit a multicast demand from a source to a set of destinations in order to balance traffic load, improving network resource utilization. Experimental results obtained by simulations using eight real network topologies show that this new approach gets trade off solutions while simultaneously considering five objective functions. As expected, when M-MMA is compared to an equivalent singletree alternative, it accommodates more traffic demand in a high traffic saturated network.IFIP International Conference on Artificial Intelligence in Theory and Practice - Evolutionary ComputationRed de Universidades con Carreras en Informática (RedUNCI)2006-08info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/23915enginfo:eu-repo/semantics/altIdentifier/isbn/0-387-34654-6info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T10:55:40Zoai:sedici.unlp.edu.ar:10915/23915Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 10:55:40.493SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Multitree-multiobjective multicast routing for traffic engineering
title Multitree-multiobjective multicast routing for traffic engineering
spellingShingle Multitree-multiobjective multicast routing for traffic engineering
Prieto, Joel
Ciencias Informáticas
traffic engineering
routing algorithm
M-MMA
GMM model
title_short Multitree-multiobjective multicast routing for traffic engineering
title_full Multitree-multiobjective multicast routing for traffic engineering
title_fullStr Multitree-multiobjective multicast routing for traffic engineering
title_full_unstemmed Multitree-multiobjective multicast routing for traffic engineering
title_sort Multitree-multiobjective multicast routing for traffic engineering
dc.creator.none.fl_str_mv Prieto, Joel
Barán, Benjamín
Crichigno, Jorge
author Prieto, Joel
author_facet Prieto, Joel
Barán, Benjamín
Crichigno, Jorge
author_role author
author2 Barán, Benjamín
Crichigno, Jorge
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
traffic engineering
routing algorithm
M-MMA
GMM model
topic Ciencias Informáticas
traffic engineering
routing algorithm
M-MMA
GMM model
dc.description.none.fl_txt_mv This paper presents a new traffic engineering multitreemultiobjective multicast routing algorithm (M-MMA) that solves for the first time the GMM model for Dynamic Multicast Groups. Multitree traffic engineering uses several trees to transmit a multicast demand from a source to a set of destinations in order to balance traffic load, improving network resource utilization. Experimental results obtained by simulations using eight real network topologies show that this new approach gets trade off solutions while simultaneously considering five objective functions. As expected, when M-MMA is compared to an equivalent singletree alternative, it accommodates more traffic demand in a high traffic saturated network.
IFIP International Conference on Artificial Intelligence in Theory and Practice - Evolutionary Computation
Red de Universidades con Carreras en Informática (RedUNCI)
description This paper presents a new traffic engineering multitreemultiobjective multicast routing algorithm (M-MMA) that solves for the first time the GMM model for Dynamic Multicast Groups. Multitree traffic engineering uses several trees to transmit a multicast demand from a source to a set of destinations in order to balance traffic load, improving network resource utilization. Experimental results obtained by simulations using eight real network topologies show that this new approach gets trade off solutions while simultaneously considering five objective functions. As expected, when M-MMA is compared to an equivalent singletree alternative, it accommodates more traffic demand in a high traffic saturated network.
publishDate 2006
dc.date.none.fl_str_mv 2006-08
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/23915
url http://sedici.unlp.edu.ar/handle/10915/23915
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/isbn/0-387-34654-6
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
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reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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