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| Presentation | 2020-08-25 10:50 Policy Gradient-based Deep Reinforcement Learning for Deadline-aware Data Transfer over Wide Area Networks Masaki Notoya, Kohei Shiomoto (Tokyo City Univ.), Takashi Kurimoto (NII)  PN2020-21 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Deadline-aware job scheduling problems have been attracting attention in the application domains of scientific workflows and commercial data-center wide area networks. The scheduling of deadline-aware jobs is not an easy task. Satisfying all of the demands before the corresponding deadlines requires optimally spreading traffic along future times. Existing methods formulate an optimization problem which in most cases is computationally expensive. The cumulative effect of running a sequence of optimization problems can be very poor since only limited knowledge of the future is available when each optimization problem is solved. In this paper, we leverage a deep reinforcement learning, Policy Gradient, to build a novel deadline-aware job scheduling method. Through the simulation, we obtain the following findings. The EDF-trained initial policy outperforms a random initial policy. The proposed method outperforms the existing methods including RR, FCFS, and EDF with wider gaps from the existing methods expand as the traffic load increases. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Deadline / High Speed Network / Data Center / Job Scheduling / Deep Reinforcement Learning / Policy Gradient / / | |
| Reference Info. | IEICE Tech. Rep., vol. 120, no. 137, PN2020-21, pp. 49-56, Aug. 2020. | |
| Paper # | PN2020-21 | |
| Date of Issue | 2020-08-17 (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 | PN2020-21 |
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| Conference Information | |
| Committee | PN |
| Conference Date | 2020-08-24 - 2020-08-25 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | PN |
| Conference Code | 2020-08-PN |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Policy Gradient-based Deep Reinforcement Learning for Deadline-aware Data Transfer over Wide Area Networks |
| Sub Title (in English) | |
| Keyword(1) | Deadline |
| Keyword(2) | High Speed Network |
| Keyword(3) | Data Center |
| Keyword(4) | Job Scheduling |
| Keyword(5) | Deep Reinforcement Learning |
| Keyword(6) | Policy Gradient |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Masaki Notoya |
| 1st Author's Affiliation | Tokyo City University (Tokyo City Univ.) |
| 2nd Author's Name | Kohei Shiomoto |
| 2nd Author's Affiliation | Tokyo City University (Tokyo City Univ.) |
| 3rd Author's Name | Takashi Kurimoto |
| 3rd Author's Affiliation | National Institute of Informatics (NII) |
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| Speaker | Author-1 |
| Date Time | 2020-08-25 10:50:00 |
| Presentation Time | 25 minutes |
| Registration for | PN |
| Paper # | PN2020-21 |
| Volume (vol) | vol.120 |
| Number (no) | no.137 |
| Page | pp.49-56 |
| #Pages | 8 |
| Date of Issue | 2020-08-17 (PN) |
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