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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/23915
Ver los metadatos del registro completo
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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:UNLP2025-09-03T10:28:25Zoai: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:13292025-09-03 10:28:26.053SEDICI (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 |
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http://sedici.unlp.edu.ar/handle/10915/23915 |
dc.language.none.fl_str_mv |
eng |
language |
eng |
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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) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-sa/2.5/ar/ Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) |
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application/pdf |
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reponame:SEDICI (UNLP) instname:Universidad Nacional de La Plata instacron:UNLP |
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score |
13.13397 |