A Bayesian approach for user aware peer-to-peer video streaming systems |
| |
Authors: | Ihsan Ullah Guillaume Doyen Grégory Bonnet Dominique Gaïti |
| |
Affiliation: | 1. Centre des Sciences du Goût et de l''Alimentation, CNRS, INRAE, Institut Agro, Université de Bourgogne Franche-Comté, Dijon F-21000, France;2. INRAE, PROBE Research Infrastructure, ChemoSens Facility, Dijon F-21000, France;3. Brewing Science Laboratories, Asahi Quality and Innovations, Ltd., 1-21 Midori 1-Chome, Moriya, Ibaraki 302-0106, Japan;1. Assistant Professor, Department of Computer Applications, Karpagam Academy of Higher Education (Deemed to be University), Coimbatore-641021, Tamilnadu State, India;2. Research Scholar, Department of Computer Applications, Karpagam Academy of Higher Education (Deemed to be University), Coimbatore-641021, Tamilnadu State, India |
| |
Abstract: | Peer-to-Peer (P2P) architectures for live video streaming has attracted a significant attention from both academia and industry. P2P design enables end-hosts to relay streams to each other overcoming the scalability issue of centralized architectures. However, these systems struggle to provide a service of comparable quality to that of traditional television. Since end-hosts are controlled by users, their behavior has a strong impact on the performance of P2P streaming systems, leading to potential service disruption and low streaming quality. Thus, considering the user behavior in these systems could bring significant performance improvements. Toward this end, we propose a Bayesian network that captures all the elements making part of the user behavior or related to it. This network is built from the information found in a cross-analysis of numerous large-scale measurement campaigns, analyzing the user behavior in video streaming systems. We validate our model through intensive simulations showing that our model can learn a user behavior and is able to predict several activities helping thus in optimizing these systems for a better performance. We also propose a method based on traces collection of the same user type that accelerates the learning process of this network. Furthermore, we evaluate the performance of this model through exploring its applications and comparison with non-contextual models. |
| |
Keywords: | |
本文献已被 ScienceDirect 等数据库收录! |
|