Multicast traffic grooming in tap-and-continue WDM mesh networks

Multicast applications are expected to be major drivers of Internet traffic growth. As most multicast connections require much lower bandwidth than the capacity offered by a wavelength, multicast traffic grooming is needed to efficiently use network resources. Recent research on multicast grooming h...

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Bibliographic Details
Main Authors: Lin, Rongping, Zhong, Wen-De, Bose, Sanjay Kumar, Zukerman, Moshe
Other Authors: School of Electrical and Electronic Engineering
Format: Article
Language:English
Published: 2014
Subjects:
Online Access:https://hdl.handle.net/10356/101627
http://hdl.handle.net/10220/18698
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Institution: Nanyang Technological University
Language: English
Description
Summary:Multicast applications are expected to be major drivers of Internet traffic growth. As most multicast connections require much lower bandwidth than the capacity offered by a wavelength, multicast traffic grooming is needed to efficiently use network resources. Recent research on multicast grooming has focused on light-trees because of their natural advantage for multicast traffic. However, using light-trees may lead to some serious negative side effects because of light splitting. In this paper, we investigate the multicast traffic grooming problem in tap-and-continue (TaC) networks, where a node can tap a small amount of incoming optical power for the local station while forwarding the remainder to an output. We first propose a simple and efficient node architecture with the TaC mechanism. We use this in an integer linear programming (ILP) formulation with the objective of minimizing the network cost in terms of the number of higher layer electronic ports and the number of wavelengths used. Since the ILP is not scalable, two heuristic algorithms, multicast trail grooming (MTG) and multiple destination trail-based grooming (MDTG), are proposed. Using the ILP, we show that having more costly nodes with multicast capability does not improve the performance significantly. The solutions obtained by MTG and MDTG are close to the ILP optimal solution. MTG and MDTG are shown to work efficiently for typical network topologies such as NSFNET, with MTG showing better performance than MDTG.