| Presentation | 2009-07-17 Distributed Lightpath Establishment based on Reinforcement Learning in WDM Networks : On Providing Service Differentiation and Effective Wavelength Utilization Izumi KOYANAGI, Takuji TACHIBANA, Kenji SUGIMOTO, |
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| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | In our proposed method, each node utilizes Q-learning, which is one of the reinforcement learning techniques, and when a new lightpath establishment request arrives, the node decides whether the lightpath request should be accepted or rejected according to the most effective action. Then, the node learns the optimal action based on the current state and the used action. After the adequate learning, the node can provide the service differentiation and the effective wavelength utilization. This method can be available when the number of wavelengths and the number of classes are large without the assumptions about the lightpath arrival process and the distribution of the lightpath holding time. We also discuss how the proposed method is used with Generalized Multi-Protocol Label Switching (GMPLS). We evaluate the performance of the proposed method with simulation. From numerical examples, we show the impacts of learning time and learning parameters on the performance of the proposed method. In addition, we evaluate the effectiveness of the proposed method and investigate the number of wavelengths with which our proposed method can be available. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Reinforcement learning / Q-learning / Dynamic lightpath establishment / Service differentiation / WDM networks |
| Paper # | NS2009-55 |
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| Conference Information | |
| Committee | NS |
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| Conference Date | 2009/7/9(1days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
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| Paper Information | |
| Registration To | Network Systems(NS) |
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| Language | ENG |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Distributed Lightpath Establishment based on Reinforcement Learning in WDM Networks : On Providing Service Differentiation and Effective Wavelength Utilization |
| Sub Title (in English) | |
| Keyword(1) | Reinforcement learning |
| Keyword(2) | Q-learning |
| Keyword(3) | Dynamic lightpath establishment |
| Keyword(4) | Service differentiation |
| Keyword(5) | WDM networks |
| 1st Author's Name | Izumi KOYANAGI |
| 1st Author's Affiliation | Graduate School of Information Science, Nara Institute of Science and Technology() |
| 2nd Author's Name | Takuji TACHIBANA |
| 2nd Author's Affiliation | Graduate School of Information Science, Nara Institute of Science and Technology |
| 3rd Author's Name | Kenji SUGIMOTO |
| 3rd Author's Affiliation | Graduate School of Information Science, Nara Institute of Science and Technology |
| Date | 2009-07-17 |
| Paper # | NS2009-55 |
| Volume (vol) | vol.109 |
| Number (no) | 129 |
| Page | pp.pp.- |
| #Pages | 6 |
| Date of Issue | |