摘要
内容分发网络在流媒体传输中广泛使用,可以解决其网络性能和服务质量存在的问题。随着流媒体市场需求越来越复杂多样,利用机器学习技术提高内容分发网络的服务质量成为一个新兴的研究热点。概括了使用机器学习融合内容分发网络并提高客户端和服务端性能的几种方法,挖掘出机器学习在内容分发网络应用中亟待解决的关键性技术问题及应对方法。提出了基于机器学习的内容分发网络的未来技术发展趋势,以期进一步提高内容分发网络的资源管理、实时分配和质量规划。
Content delivery networks(CDN)are widely used in stream media transmission to solve problems exist in network performance and quality of service(QOS).With the requirements for stream media market becoming more and more complex,it is an emerging research direction is to explore the use of machine learning techniques to improve the quality of CDNs.Several methods to improve client and server performance in a machine learning-integrated CDN are summarized.Unsolved problems and key technologies in machine learning-integrated CDN are discussed.Future development trend of CDNs based on machine learning is proposed,so as to further improve the resource management,real-time distribution and quality planning of CDNs.
作者
吕慧
许力澜
王瑞琨
禹忠
LYU Hui;XU Lilan;WANG Ruikun;YU Zhong(Shaanxi Branch,China Tower Corporation Limited Shaanxi Branch,Xi’an 710065,China;School of Telecommunication and Information Engineer,Xi’an University of Posts and Telecommunications,Xi’an 710121,China)
出处
《西安邮电大学学报》
2022年第1期29-34,59,共7页
Journal of Xi’an University of Posts and Telecommunications
基金
陕西省重点产业链项目(2018ZDXL-GY-04-01)。
关键词
内容分发网络
机器学习
流媒体网络
自适应比特率
content delivery networks
machine learning
stream media network
adaptive bit rate
