@inproceedings{7ab85aa71098424abfbfc0fcba77365f,
title = "P2PTV Traffic Classification and Its Characteristic Analysis Using Machine Learning",
abstract = "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.",
keywords = "Clustering, Machine learning, P2P, P2PTV, Traffic analysis",
author = "Koji Hayashi and Rina Ooka and Takumi Miyoshi and Taku Yamazaki",
note = "Funding Information: ACKNOWLEDGMENT This study was supported by JSPS KAKENHI Grant Number 17K06441.; 20th Asia-Pacific Network Operations and Management Symposium, APNOMS 2019 ; Conference date: 18-09-2019 Through 20-09-2019",
year = "2019",
month = sep,
doi = "10.23919/APNOMS.2019.8892948",
language = "English",
series = "2019 20th Asia-Pacific Network Operations and Management Symposium: Management in a Cyber-Physical World, APNOMS 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2019 20th Asia-Pacific Network Operations and Management Symposium",
}