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| Presentation | 2019-11-14 17:45 Reinforcement-learning-based optical-path routing and wavelength assignment with adaptation to traffic-distribution change Ryuta Shiraki, Yojiro Mori, Hiroshi Hasegawa (Nagoya Univ.), Ken-ichi Sato (AIST)  PN2019-27 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | We propose a novel RSA algorithm that can increase fiber-utilization efficiency in dynamically-changing flexible-grid networks. The proposed RSA scheme combines semi-flexible spectrum assignment with network-state-value evaluation. First, to relax the computational complexity of the ML process, we adopt semi-flexible spectrum assignment in which each type of bitrate signal can be set only to the fixed grid that is equal to the minimum required slot width. Since the grid interval for each bitrate channel can be regarded as a fixed grid, the number of input vectors is reduced. Consequently, the ML process can be executed with feasible computation cost. In the learning process, the network-state value is calculated based on the number of path demands that can be accommodated by the presently unused frequency slots. RSA is then applied to each path so that the network-state value after optical-path setup is maximized. The effectiveness of the proposed RSA algorithm is verified by numerical simulations using various network parameters such as network topologies, traffic-churn rates, and traffic-demand distributions. The number of path demands accommodated to the examined networks is increased by 17.1% compared to the conventional RSA scheme based on a heuristic algorithm. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Photonic network / Flexible grid / Routing and spectrum assignment / Machine learning / Reinforcement learning / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 290, PN2019-27, pp. 23-27, Nov. 2019. | |
| Paper # | PN2019-27 | |
| Date of Issue | 2019-11-07 (PN) | |
| ISSN | Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | PN2019-27 |
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| Conference Information | |
| Committee | PN |
| Conference Date | 2019-11-14 - 2019-11-15 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | PN |
| Conference Code | 2019-11-PN |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Reinforcement-learning-based optical-path routing and wavelength assignment with adaptation to traffic-distribution change |
| Sub Title (in English) | |
| Keyword(1) | Photonic network |
| Keyword(2) | Flexible grid |
| Keyword(3) | Routing and spectrum assignment |
| Keyword(4) | Machine learning |
| Keyword(5) | Reinforcement learning |
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| 1st Author's Name | Ryuta Shiraki |
| 1st Author's Affiliation | Nagoya University (Nagoya Univ.) |
| 2nd Author's Name | Yojiro Mori |
| 2nd Author's Affiliation | Nagoya University (Nagoya Univ.) |
| 3rd Author's Name | Hiroshi Hasegawa |
| 3rd Author's Affiliation | Nagoya University (Nagoya Univ.) |
| 4th Author's Name | Ken-ichi Sato |
| 4th Author's Affiliation | National Institute of Advanced Industrial Science and Technology (AIST) |
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| Speaker | Author-1 |
| Date Time | 2019-11-14 17:45:00 |
| Presentation Time | 25 minutes |
| Registration for | PN |
| Paper # | PN2019-27 |
| Volume (vol) | vol.119 |
| Number (no) | no.290 |
| Page | pp.23-27 |
| #Pages | 5 |
| Date of Issue | 2019-11-07 (PN) |
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