IEEE GLOBECOM 2007-2007 IEEE Global Telecommunications Conference 2007
DOI: 10.1109/glocom.2007.487
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Multi-Layer Network Design with Multicast Traffic and Statistical Multiplexing

Abstract: The new packet services and applications are radically changing not only traffic demands but also the architecture of transport networks. Adding packet layer support into backbone nodes is considered an important opportunity by telecom carriers that want to integrate packet technologies of the access and metropolitan networks within their networks to improve efficiency and flexibility. In this context, MultiProtocol Label Switching (MPLS) is at the moment the most promising and popular packet technology. The r… Show more

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Cited by 8 publications
(7 citation statements)
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“…Capone et al [3,7] have studied multi-layered design with statistical multiplexing: routing different commodities on the same capacities results in cancellations of those demands variations . They compute a lower bound through a lagrangian relaxation, and use heuristics to find good upper bounds.…”
Section: Introductionmentioning
confidence: 99%
“…Capone et al [3,7] have studied multi-layered design with statistical multiplexing: routing different commodities on the same capacities results in cancellations of those demands variations . They compute a lower bound through a lagrangian relaxation, and use heuristics to find good upper bounds.…”
Section: Introductionmentioning
confidence: 99%
“…Following another line, some authors have proposed to use robust optimization models to approximate traffic multiplexing, while dealing with network routing end dimensioning [5]. However, these approaches remain rough, and fail to model the end-to-end stochastic behavior of flows in a meshed network.…”
Section: Introductionmentioning
confidence: 98%
“…Interestingly, some more recent papers have proposed to use robust optimization models to approximate such traffic multiplexing, while dealing with network routing end dimensioning [5,1]. However, these approaches remain rough, and fail to model the end-to-end stochastic behavior of flows in a meshed network.…”
Section: Introductionmentioning
confidence: 98%