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Paper Abstract and Keywords
Presentation 2021-01-22 15:45
A Study of Caching Policy with Cache Hit Prediction for User Generated Video
Meguru Yamazaki, Miki Yamamoto (Kansai Univ.) NS2020-121
Abstract (in Japanese) (See Japanese page) 
(in English) With wide deployment of video platforms on which users can upload their generating videos, such as YouTube and niconico, User-Generated Content (UGC) has been attracting a lot of attention. UGC is reported to have different characteristics of popularity from commercial video delivery services such as Netflix and Hulu. Popularity of some UGC has a tendency of short-term temporal variation. This feature degrades the performance of caching methods such as widely deployed LRU. New caching methods taking account of manually analyzed viewing tendency of UGCs have been proposed. Viewing tendencies are generally quite different for platforms, regions and times. It is difficult to respond to these diverse UGC dynamics in these existing proposals because of complexity of manual analysis of viewing tendency and manual tuning of caching strategy. In this paper, we newly propose a machine learning approach for caching policy for UGCs. In our approach, cache hit ratio of the content is predicted and caching policy is designed by using this predicted cache hit ratio. Our performance evaluation results using YouTube traces show that our proposed method can improve cache performance when compared with several conventional approaches.
Keyword (in Japanese) (See Japanese page) 
(in English) Cache / UGC / Machine Learning / / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 327, NS2020-121, pp. 66-69, Jan. 2021.
Paper # NS2020-121 
Date of Issue 2021-01-14 (NS) 
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 NS2020-121

Conference Information
Committee NS NWS  
Conference Date 2021-01-21 - 2021-01-22 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Network software (Software architecture, Middleware), Network application, SOA/SDP, NGN/IMS/API, Distributed control/Dynamic routing, Grid, NFV, IoT, Network/System reliability, Network/System evaluation, etc. 
Paper Information
Registration To NS 
Conference Code 2021-01-NS-NWS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study of Caching Policy with Cache Hit Prediction for User Generated Video 
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Keyword(1) Cache  
Keyword(2) UGC  
Keyword(3) Machine Learning  
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1st Author's Name Meguru Yamazaki  
1st Author's Affiliation Kansai University (Kansai Univ.)
2nd Author's Name Miki Yamamoto  
2nd Author's Affiliation Kansai University (Kansai Univ.)
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Speaker Author-1 
Date Time 2021-01-22 15:45:00 
Presentation Time 25 minutes 
Registration for NS 
Paper # NS2020-121 
Volume (vol) vol.120 
Number (no) no.327 
Page pp.66-69 
#Pages
Date of Issue 2021-01-14 (NS) 


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