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Paper Abstract and Keywords
Presentation 2026-03-06 10:10
Multimodal Deep Learning-Based Routing for Congestion Avoidance
Shota Kaneko, Ken-ichi Baba (Kogakuin Univ.) IN2025-96
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, with the widespread adoption of cloud services, video streaming, and IoT technologies, the volume of traffic flowing through networks has increased significantly, leading to large-scale and increasingly complex network structures. In such environments, congestion caused by traffic concentration on specific links has become a critical issue, and traffic engineering aimed at efficiently utilizing limited network resources has attracted considerable attention.Existing studies have proposed methods that design routing paths in a centralized manner using deep learning based on global network traffic information. However, these approaches require complete traffic information, resulting in challenges related to computational cost and scalability.To address these issues, this paper proposes a routing control method based on multimodal deep learning that uses partial traffic demand information and link utilization as inputs. Through simulation-based evaluations, we demonstrate that the proposed method suppresses congestion more effectively than shortest-path routing and achieves efficient load balancing even when only partial information is available.
Keyword (in Japanese) (See Japanese page) 
(in English) Traffic Engineering / Deep Learning / Multimodal Learning / Congestion Avoidance / Routing / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 386, IN2025-96, pp. 204-209, March 2026.
Paper # IN2025-96 
Date of Issue 2026-02-25 (IN) 
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 IN2025-96

Conference Information
Committee IN NS  
Conference Date 2026-03-04 - 2026-03-06 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa-Ken Shichoson Jichi Kaikan 
Topics (in Japanese) (See Japanese page) 
Topics (in English) General 
Paper Information
Registration To IN 
Conference Code 2026-03-IN-NS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Multimodal Deep Learning-Based Routing for Congestion Avoidance 
Sub Title (in English)  
Keyword(1) Traffic Engineering  
Keyword(2) Deep Learning  
Keyword(3) Multimodal Learning  
Keyword(4) Congestion Avoidance  
Keyword(5) Routing  
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1st Author's Name Shota Kaneko  
1st Author's Affiliation Kogakuin University (Kogakuin Univ.)
2nd Author's Name Ken-ichi Baba  
2nd Author's Affiliation Kogakuin University (Kogakuin Univ.)
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Speaker Author-1 
Date Time 2026-03-06 10:10:00 
Presentation Time 25 minutes 
Registration for IN 
Paper # IN2025-96 
Volume (vol) vol.125 
Number (no) no.386 
Page pp.204-209 
#Pages 6 
Date of Issue 2026-02-25 (IN) 

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