A foresighted strategy for greed-based multicasting algorithms in all-optical mesh networks

A foresighted strategy for greed-based multicasting algorithms in all-optical mesh networks For abundant bandwidth, all-optical mesh networks have been more and more important in communications, and multicasting is one of the key technologies to that. The problem to find a minimum multicasting tree is NP-hard, and all the existing algorithms are heuristics. Most of them are based on the idea of being greed. A greedy idea is always shortsighted. While it could get a good local effect, it would obtain a somewhat bad global performance. In this article, we propose a foresighted strategy for greed-based multicasting algorithms. With the co-action of the greedy idea and a foresighted strategy, a multicasting algorithm can get a good local and global performance simultaneously. We introduce the strategy by embedding it in the Member-Only algorithm and investigate two indexes, the average cost of all multicasting requests and the blocking rate of the whole network. Simulation results show that, with the presence of the proposed foresighted strategy, these two targets are all obviously decreased. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Photonic Network Communications Springer Journals

A foresighted strategy for greed-based multicasting algorithms in all-optical mesh networks

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Publisher
Springer US
Copyright
Copyright © 2010 by Springer Science+Business Media, LLC
Subject
Computer Science; Characterization and Evaluation of Materials; Electrical Engineering; Computer Communication Networks
ISSN
1387-974X
eISSN
1572-8188
D.O.I.
10.1007/s11107-010-0269-8
Publisher site
See Article on Publisher Site

Abstract

For abundant bandwidth, all-optical mesh networks have been more and more important in communications, and multicasting is one of the key technologies to that. The problem to find a minimum multicasting tree is NP-hard, and all the existing algorithms are heuristics. Most of them are based on the idea of being greed. A greedy idea is always shortsighted. While it could get a good local effect, it would obtain a somewhat bad global performance. In this article, we propose a foresighted strategy for greed-based multicasting algorithms. With the co-action of the greedy idea and a foresighted strategy, a multicasting algorithm can get a good local and global performance simultaneously. We introduce the strategy by embedding it in the Member-Only algorithm and investigate two indexes, the average cost of all multicasting requests and the blocking rate of the whole network. Simulation results show that, with the presence of the proposed foresighted strategy, these two targets are all obviously decreased.

Journal

Photonic Network CommunicationsSpringer Journals

Published: Aug 11, 2010

References

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