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清华大学学报(自然科学版)  2017, Vol. 57 Issue (10): 1056-1062    DOI: 10.16511/j.cnki.qhdxxb.2017.25.045
  计算机科学与技术 本期目录 | 过刊浏览 | 高级检索 |
面向海量流媒体信道资源分配快速Nash议价算法
刘扬, 魏蔚
河南工业大学 信息科学与工程学院, 郑州 450001
Fast Nash bargaining algorithm for resource scheduling problems with a large number of media streaming channels
LIU Yang, WEI Wei
College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China
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摘要 在大规模在线流媒体分发系统中,服务端需处理来自全球各区域的海量用户请求。现有混合云架构不能很好地满足日益增加的动态流媒体内容分发要求,需结合私有数据中心、云和内容分发网络3类平台,充分挖掘各平台的优势以降低费用并提高服务质量。针对基于3种平台的混合云,该文给出了多资源分配问题的描述,将其转化为Nash议价问题,从几何角度获取问题的高效求解算法,并基于实际商用环境中海量流媒体采样数据进行了模拟实验。实验结果表明:相比传统的混合云架构,该算法可显著提升服务质量,在动态和静态内容混合情况下可降低平均约40%的费用,可在包括大量动态流媒体内容场景中进行快速有效的资源分配。
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刘扬
魏蔚
关键词 流媒体云计算混合云资源调度Nash议价    
Abstract:The servers in large media streaming systems need to handle a large number of requests from all around the world. However, due to the increasing dynamic media content and because existing cloud-based architectures cannot provide enough benefits, the service provider needs to utilize a hybrid architecture composed of a content delivery network with private and cloud data centers to provide sufficient quality of service while reducing costs. This paper describes a general resource scheduling problem for this scenario for a hybrid cloud, which is then transformed into a Nash bargaining problem. A fast Nash bargaining algorithm is given based on a geometrical perspective of the problem. Tests show that the algorithm improves the quality of service and reduces expenses by about 40% compared with a traditional hybrid architecture, so it can effectively handle large amounts of dynamic media content.
Key wordsstreaming media    cloud computing    hybrid cloud    resource scheduling    Nash bargaining
收稿日期: 2016-10-24      出版日期: 2017-10-15
ZTFLH:  TP301.6  
通讯作者: 魏蔚,副教授,E-mail:nsyncw@126.com     E-mail: nsyncw@126.com
引用本文:   
刘扬, 魏蔚. 面向海量流媒体信道资源分配快速Nash议价算法[J]. 清华大学学报(自然科学版), 2017, 57(10): 1056-1062.
LIU Yang, WEI Wei. Fast Nash bargaining algorithm for resource scheduling problems with a large number of media streaming channels. Journal of Tsinghua University(Science and Technology), 2017, 57(10): 1056-1062.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2017.25.045  或          http://jst.tsinghuajournals.com/CN/Y2017/V57/I10/1056
  图1 系统结构示意图
  图2 几何空间中天平均衡态示意图
  图3 YouTube采样数据中每视频观看次数概率密度函数图
  图4 各方法未满足请求比例与动态内容比例关系图
  图5 不同参数设定GBMS与纯云计算方式费用比
  图6 GBMS费用与纯云计算方式跨区域流量比
  图7 GBMS费用与纯云计算方式费用和流量比
  图8 GBMS中不同参数下调度费用对比
  图9 GBMS中不同参数下跨域流量对比
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