P2PTV Traffic Classification and Its Characteristic Analysis Using Machine Learning

DOI

説明

This paper proposes a classification method for peer- to-peer video streaming (P2PTV) traffic using machine learning. Since the user terminals (peers) share video data in P2PTV, P2PTV traffic is difficult to control and manage statically as both the number of peers sharing the same video data and the throughput vary with respect to contents. Although there exists a conventional method to classify and model P2PTV traffic by focusing on the number of peers and throughput, problems on the classification criteria and reproducibility remain in this method. In this paper, we use a clustering method that is considered as one of the machine learning methods and try to classify P2PTV traffic data into some categories. We extracted 18 features by analyzing P2PTV traffic of popular P2PTV applications: PPStream and PPTV; and then classified the traffic. The classification results show that about 400 traffic data sets were categorized into four clusters.

収録刊行物

  • IEICE Proceeding Series

    IEICE Proceeding Series 59 TS8-4-, 2019-09-18

    The Institute of Electronics, Information and Communication Engineers

キーワード

詳細情報 詳細情報について

  • CRID
    1391131406291457152
  • NII論文ID
    230000011870
  • DOI
    10.34385/proc.59.ts8-4
  • ISSN
    21885079
  • 本文言語コード
    en
  • データソース種別
    • JaLC
    • CiNii Articles
  • 抄録ライセンスフラグ
    使用不可

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