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Paper Abstract and Keywords
Presentation 2019-05-23 16:20
Characteristic Analysis of Time-series P2PTV Traffic Using Machine Learning
Rina Ooka, Koji Hayashi, Takumi Miyoshi, Taku Yamazaki (Shibaura Inst. of Tech.) ICM2019-2
Abstract (in Japanese) (See Japanese page) 
(in English) P2P-based video streaming service (P2PTV) in which user terminals (peers) directly communicate with each other has attracted attention due to the increase of users who enjoy video distribution services.
P2PTV can distribute the data delivery load concentrated to the video servers since peers share and transfer the video data among them.
To maintain the network properly, it is necessary to understand the characteristics of P2PTV traffic in advance. Since each video content has a different popularity and data size, the number of peers that share the same video data and the throughput may greatly fluctuate.
In our previous studies, we have obtained P2PTV traffic in watching each video content for a long time and analyzed the characteristics by classifying the obtained traffic data.
However, users would participate in or leave from P2PTV services dynamically, and then the traffic characteristics may change from moment to moment: The classification and analysis of traffic characteristics on a per content basis must be therefore insufficient.
In this paper, we propose a time-series P2PTV traffic classification method. The proposed method divides P2PTV traffic into short-time data pieces to create time series data, and classifies these data by machine learning.
We also analyze traffic characteristics from the classification results of 80 P2PTV traffic data.
Keyword (in Japanese) (See Japanese page) 
(in English) P2P / P2PTV / Machine learning / Traffic analysis / Clustering / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 52, ICM2019-2, pp. 31-36, May 2019.
Paper # ICM2019-2 
Date of Issue 2019-05-16 (ICM) 
ISSN Online edition: ISSN 2432-6380
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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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Conference Information
Committee ICM IPSJ-CSEC IPSJ-IOT  
Conference Date 2019-05-23 - 2019-05-24 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To ICM 
Conference Code 2019-05-ICM-CSEC-IOT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Characteristic Analysis of Time-series P2PTV Traffic Using Machine Learning 
Sub Title (in English)  
Keyword(1) P2P  
Keyword(2) P2PTV  
Keyword(3) Machine learning  
Keyword(4) Traffic analysis  
Keyword(5) Clustering  
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1st Author's Name Rina Ooka  
1st Author's Affiliation Shibaura Institute of Technology (Shibaura Inst. of Tech.)
2nd Author's Name Koji Hayashi  
2nd Author's Affiliation Shibaura Institute of Technology (Shibaura Inst. of Tech.)
3rd Author's Name Takumi Miyoshi  
3rd Author's Affiliation Shibaura Institute of Technology (Shibaura Inst. of Tech.)
4th Author's Name Taku Yamazaki  
4th Author's Affiliation Shibaura Institute of Technology (Shibaura Inst. of Tech.)
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Speaker Author-1 
Date Time 2019-05-23 16:20:00 
Presentation Time 25 minutes 
Registration for ICM 
Paper # ICM2019-2 
Volume (vol) vol.119 
Number (no) no.52 
Page pp.31-36 
#Pages
Date of Issue 2019-05-16 (ICM) 


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