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Paper Abstract and Keywords
Presentation 2019-03-05 09:20
Characteristic Analysis of P2PTV Traffic and Its Classification Using Machine Learning
Koji Hayashi, Takumi Miyoshi (Shibaura Inst. Tech.) NS2018-230
Abstract (in Japanese) (See Japanese page) 
(in English) Peer-to-peer video streaming service (P2PTV) has attracted attention due to the huge demands for video streaming services. Since the user terminals (peers) share data, it is possible to distribute the load concentration on the server in P2PTV. However,
P2PTV traffic is difficult to control and manage statically as both the number of peers sharing video data and the throughput vary with respect to each content. A method to classify and model P2PTV traffic by focusing on the number of peers and throughput has been studied. However, the classification criteria are ambiguous, and there is no reproducibility in this study because the authors tried to subjectively classify P2PTV traffic. Clustering, one of machine learning methods, can classify a large amount of data into some categories by calculating the similarity based on the characteristic values of input data. This paper proposes a P2PTV traffic classification method using machine learning. We examined the features extracted by analyzing the characteristics of traffic and then classified traffic data by the clustering method in PPStream and PPTV, typical P2PTV applications. In the characteristics analysis, we examined 18 kinds of features extracted from P2PTV traffic that was obtained when we watched video contents. The classification results in each application show that about 400 P2PTV traffic data sets can be categorized into four clusters.
Keyword (in Japanese) (See Japanese page) 
(in English) P2P / P2PTV / Traffic analysis / Machine learning / Clustering / / /  
Reference Info. IEICE Tech. Rep., vol. 118, no. 465, NS2018-230, pp. 219-224, March 2019.
Paper # NS2018-230 
Date of Issue 2019-02-25 (NS) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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 NS2018-230

Conference Information
Committee IN NS  
Conference Date 2019-03-04 - 2019-03-05 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Convention Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English) General 
Paper Information
Registration To NS 
Conference Code 2019-03-IN-NS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Characteristic Analysis of P2PTV Traffic and Its Classification Using Machine Learning 
Sub Title (in English)  
Keyword(1) P2P  
Keyword(2) P2PTV  
Keyword(3) Traffic analysis  
Keyword(4) Machine learning  
Keyword(5) Clustering  
1st Author's Name Koji Hayashi  
1st Author's Affiliation Shibaura Institute of Technology (Shibaura Inst. Tech.)
2nd Author's Name Takumi Miyoshi  
2nd Author's Affiliation Shibaura Institute of Technology (Shibaura Inst. Tech.)
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Date Time 2019-03-05 09:20:00 
Presentation Time 20 
Registration for NS 
Paper # IEICE-NS2018-230 
Volume (vol) IEICE-118 
Number (no) no.465 
Page pp.219-224 
#Pages IEICE-6 
Date of Issue IEICE-NS-2019-02-25 

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