Paper Abstract and Keywords |
Presentation |
2020-03-03 09:00
Research of Scheduling Method Considering Traffic-Characteristics for Data Center Wide Area Network(DC-WAN) Takashi Arakawa, Kohei Shiomoto (TCU), Takashi Kurimoto (NII) PN2019-61 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
Recent years, the demand for efficient use of Data-Center Wide Area Network (DC-WAN) is increasing.
Since delay affects various services (eg, if search results has delayed, sales will decrease on EC site), job scheduling has attracted attention in order to achieve high efficiency and low delay of DC-WAN at the same time.
In this study, we proposed a learning method to learn the optimal job scheduling in DC-WAN with high link utilization using Policy Gradient, a Deep Reinforcement Learning algorithm.
The proposed method assumes that the job value is inversely proportional to the deadline excess time, and aims to maximize the job value.
The evaluation experiments showed that the Policy Gradient can be used to learn the optimal scheduling algorithm.
The proposed method increases the value of jobs by up to 1.7 times, compared with the Earliest Deadline First (EDF) algorithm, which is a dynamic scheduling algorithm. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
DC-WAN / job scheduling / Deep Reinforcement Learning / Policy Gradient / / / / |
Reference Info. |
IEICE Tech. Rep., vol. 119, no. 442, PN2019-61, pp. 51-58, March 2020. |
Paper # |
PN2019-61 |
Date of Issue |
2020-02-24 (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) |
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PN2019-61 |
Conference Information |
Committee |
PN |
Conference Date |
2020-03-02 - 2020-03-03 |
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(See Japanese page) |
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Paper Information |
Registration To |
PN |
Conference Code |
2020-03-PN |
Language |
Japanese |
Title (in Japanese) |
(See Japanese page) |
Sub Title (in Japanese) |
(See Japanese page) |
Title (in English) |
Research of Scheduling Method Considering Traffic-Characteristics for Data Center Wide Area Network(DC-WAN) |
Sub Title (in English) |
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DC-WAN |
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job scheduling |
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Deep Reinforcement Learning |
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Policy Gradient |
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1st Author's Name |
Takashi Arakawa |
1st Author's Affiliation |
Tokyo City University (TCU) |
2nd Author's Name |
Kohei Shiomoto |
2nd Author's Affiliation |
Tokyo City University (TCU) |
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Takashi Kurimoto |
3rd Author's Affiliation |
National Institute of Informatics (NII) |
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Speaker |
Author-1 |
Date Time |
2020-03-03 09:00:00 |
Presentation Time |
25 minutes |
Registration for |
PN |
Paper # |
PN2019-61 |
Volume (vol) |
vol.119 |
Number (no) |
no.442 |
Page |
pp.51-58 |
#Pages |
8 |
Date of Issue |
2020-02-24 (PN) |
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