高速网络环境下的P2P流媒体业务分析和识别方法

• 作 者: 陈陆颖 丛蓉 杨洁 于华
• 机 构: School of Information and Communication Engineering, Beijing University of Posts and Telecommunicati
• 出 处: 中国通信
• 发表日期: 2011年5期
• 关键词: traffic classification machine learning P2P streaming packet sampling deep flow

The growing P2P streaming traffic brings a variety of problems and challenges to ISP networks and service providers. A P2P streaming traffic classification method based on sampling technology is presented in this paper. By analyzing traffic statistical features and network behavior of P2P streaming, a group of flow characteristics were found, which can make P2P streaming more recognizable among other applications. Attributes from Netflow and those proposed by us are compared in terms of classification accuracy, and so are the results of different sampling rates. It is proved that the unified classification model with the proposed attributes can identify P2P streaming quickly and efficiently in the online system. Even with 150 sampling rate, the recognition accuracy can be higher than 94%. Moreover, we have evaluated the CPU resources, storage capacity and time consumption before and after the sampling, it is shown that the classification model after the sampling can significantly reduce the resource requirements with the same recognition accuracy.