| Presentation | 2019-05-23 Characteristic Analysis of Time-series P2PTV Traffic Using Machine Learning Rina Ooka, Koji Hayashi, Takumi Miyoshi, Taku Yamazaki, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(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) |
| Keyword(in English) | P2P / P2PTV / Machine learning / Traffic analysis / Clustering |
| Paper # | ICM2019-2 |
| Date of Issue | 2019-05-16 (ICM) |
| Conference Information | |
| Committee | ICM / IPSJ-CSEC / IPSJ-IOT |
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| Conference Date | 2019/5/23(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Kiyohito Yoshihara(KDDI Research) |
| Vice Chair | Takumi Miyoshi(Shibaura Inst. of Tech.) / Yoichi Sato(NEC) |
| Secretary | Takumi Miyoshi(NTT) / Yoichi Sato(Hitachi) |
| Assistant | Yunchen Zhu(NTT) |
| Paper Information | |
| Registration To | Technical Committee on Information and Communication Management / Special Interest Group on Computer Security / Special Interest Group on Internet and Operation Technology |
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| Language | JPN |
| 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 |
| 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.) |
| Date | 2019-05-23 |
| Paper # | ICM2019-2 |
| Volume (vol) | vol.119 |
| Number (no) | ICM-52 |
| Page | pp.pp.31-36(ICM), |
| #Pages | 6 |
| Date of Issue | 2019-05-16 (ICM) |