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Paper Abstract and Keywords
Presentation 2019-09-06 13:55
Dynamic Virtual Resource Allocation Method Using Multi-agent Deep Reinforcement Learning
Akito Suzuki, Shigeaki Harada (NTT) IN2019-29
Abstract (in Japanese) (See Japanese page) 
(in English) The network traffic demands have been changing dramatically in recent years due to the growth of various types of network service, e.g., high-quality video delivery and OS update. In order to maximize the utilization efficiency of limited network resources, network resource control technology is required to take a smooth and quick operation when the traffic demands changes. In this paper, we aim to develop the dynamic network resource control method using multi-agent deep reinforcement learning, which method can quickly optimize the network resources even when traffic demands changing drastically by learning the relationship between traffic demands pattern and optimal control in advance.
Keyword (in Japanese) (See Japanese page) 
(in English) NFV / Deep Reinforcement Learning / Network Control / / / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 195, IN2019-29, pp. 35-40, Sept. 2019.
Paper # IN2019-29 
Date of Issue 2019-08-29 (IN) 
ISSN Print edition: ISSN 0913-5685  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. (No. 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF IN2019-29

Conference Information
Committee NS IN CS NV  
Conference Date 2019-09-05 - 2019-09-06 
Place (in Japanese) (See Japanese page) 
Place (in English) Research Institute of Electrical Communication, Tohoku Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Session management (SIP/IMS), Interoperability/Standardization, NGN/NwGN/Future networks, Cloud/Data center networks, SDN (OpenFlow, etc.)/NFV, IPv6, Machine learning, etc. 
Paper Information
Registration To IN 
Conference Code 2019-09-NS-IN-CS-NV 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Dynamic Virtual Resource Allocation Method Using Multi-agent Deep Reinforcement Learning 
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Keyword(1) NFV  
Keyword(2) Deep Reinforcement Learning  
Keyword(3) Network Control  
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1st Author's Name Akito Suzuki  
1st Author's Affiliation NTT (NTT)
2nd Author's Name Shigeaki Harada  
2nd Author's Affiliation NTT (NTT)
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Speaker
Date Time 2019-09-06 13:55:00 
Presentation Time 25 
Registration for IN 
Paper # IEICE-IN2019-29 
Volume (vol) IEICE-119 
Number (no) no.195 
Page pp.35-40 
#Pages IEICE-6 
Date of Issue IEICE-IN-2019-08-29 


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