Combination between DASH and SVM machine learning in the field of video delivery

2018 4th International Conference on Environmental Science and Material Application

生态环境科学;材料科学

Chen, Jian^1 ; Yuan, Chunming^1

Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, China^1

关键词: Classification accuracy;    Feature analysis;    Network traffic;    Packet arrival time;    Packet size distribution;    Streaming media;    Traffic identification;    Video delivery;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/252/4/042036/pdf
DOI  :  10.1088/1755-1315/252/4/042036

来源: IOP

【 摘 要 】

With the development of network and streaming media technology, network video traffic is growing rapidly. In order to better control and manage network traffic and guarantee the quality of service of network video, it is necessary to classify network video services effectively. In traffic identification and classification, feature analysis and acquisition of better features are the key points to achieve efficient classification. Starting with the characteristics of packet size distribution, rate, IP alternation, byte number ratio between downstream and upstream, number of sub-stream fragments and average packet arrival time interval, this paper uses Support Vector Machine (SVM) to verify the classification effect of this feature, and achieves a high classification accuracy.