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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Haeri, S. Thong, W.W.-K. Guanrong Chen Trajkovic, L. |
| Copyright Year | 2013 |
| Description | Author affiliation: City Univ. of Hong Kong, Hong Kong, China (Thong, W.W.-K.; Guanrong Chen) || Simon Fraser Univ., Vancouver, BC, Canada (Haeri, S.; Trajkovic, L.) |
| Abstract | In this paper, we propose a Q-learning based deflection routing algorithm that may be employed to resolve contention in optical burst-switched networks. The main goal of deflection routing is to successfully deflect a burst based only on a limited knowledge that network nodes possess about their environment. Q-learning, one of the reinforcement learning algorithms, has been proposed in the past to help generate deflection decisions. The complexity of existing reinforcement learning-based deflection routing algorithms depends on the number of nodes in the network. The proposed algorithm scales well for larger networks because its complexity depends on the node degree rather than the network size. The algorithm is implemented using the ns-3 network simulator. Simulation results show that it has comparable performance to an existing reinforcement learning deflection routing scheme while having lower memory requirements. |
| Starting Page | 474 |
| Ending Page | 481 |
| File Size | 133988 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781479910502 |
| DOI | 10.1109/IRI.2013.6642508 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-08-14 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Routing Algorithm design and analysis SONET Optical switches Learning (artificial intelligence) Optical wavelength conversion Optical fiber networks |
| Content Type | Text |
| Resource Type | Article |
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