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Paper Abstract and Keywords
Presentation 2025-11-10 09:55
Reliable Cache Control in Unreliable Edge Environments Using Reinforcement Learning
Li Zheng, Kamiyama Noriaki (Ritsumeikan Univ) CCS2025-33
Abstract (in Japanese) (See Japanese page) 
(in English) Mobile Edge Cache (MEC) is a technology that installs cache servers (ES: Edge Servers) at wireless base stations and stores popular content on ESs located close to users. This approach reduces search latency, network congestion, and the number of requests to remote content providers. However, since the number of wireless base stations is extremely large, MEC generally employs low-cost and low-reliability ESs, and the resulting increase in unavailability due to failures has become a significant issue. Previous studies on unreliable ESs have used erasure coding to improve the availability of cached contents, but most of these studies assume that content is statically pre-placed on ESs. In practice, however, ESs commonly use dynamic cache control based on replacement policies such as LRU (Least Recently Used). Therefore, the authors proposed a content insertion method that combines erasure coding with LRU-based dynamic replacement, while considering the failure rate of unreliable ESs, to enhance content availability. However, we assumed a static demand case. Therefore, in this paper, assuming dynamic demand case, we employ a reinforcement learning approach to optimize the cache insertion probability fm for each
content so as to minimize the difference between the target hit rate ˆ hm and the actual hit rate hm Through numerical experiments, we compare the optimized fm obtained by the proposed method with a baseline method that always caches content upon cache misses, and the results confirm that the proposed method significantly improves the average successful recovery rate, i.e., the ratio of contents successfully reconstructed using only chunks obtained from ESs
Keyword (in Japanese) (See Japanese page) 
(in English) Mobile Edge Cache （MEC） / erasure coding / reinforcement learning / / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 235, CCS2025-33, pp. 6-11, Nov. 2025.
Paper # CCS2025-33 
Date of Issue 2025-11-03 (CCS) 
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 CCS2025-33

Conference Information
Committee CCS  
Conference Date 2025-11-10 - 2025-11-11 
Place (in Japanese) (See Japanese page) 
Place (in English) The Kanazawa Theater 
Topics (in Japanese) (See Japanese page) 
Topics (in English) CCS, etc. 
Paper Information
Registration To CCS 
Conference Code 2025-11-CCS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Reliable Cache Control in Unreliable Edge Environments Using Reinforcement Learning 
Sub Title (in English)  
Keyword(1) Mobile Edge Cache （MEC）  
Keyword(2) erasure coding  
Keyword(3) reinforcement learning  
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1st Author's Name Li Zheng  
1st Author's Affiliation Ritsumeikan University (Ritsumeikan Univ)
2nd Author's Name Kamiyama Noriaki  
2nd Author's Affiliation Ritsumeikan University (Ritsumeikan Univ)
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Speaker Author-1 
Date Time 2025-11-10 09:55:00 
Presentation Time 25 minutes 
Registration for CCS 
Paper # CCS2025-33 
Volume (vol) vol.125 
Number (no) no.235 
Page pp.6-11 
#Pages 6 
Date of Issue 2025-11-03 (CCS) 

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