摘要
随着流媒体应用的指数级增长,提高流媒体服务器的性能越发重要。本文提出一种流媒体集群服务器的负载调度策略。综合运用多级模糊系统和人工神经网络技术,对用户请求响应延时进行模糊预测,并考虑节点服务器的流媒体节目前缀缓存和补丁预取技术,提出基于响应延时阈值和请求流媒体节目在节点服务器的缓存情况的负载调度算法,并对该算法进行仿真验证。
As the application of streaming media increases exponentially, it is more and more important to improve the performance of streaming server. The paper proposes a load schedule strategy of streaming media server cluster, which comprehensively applies the multi-stage fuzzy system and the technique of artificial neural network to carry on fuzzy prediction for the user request of response delay, as considering prefix caching and patch prefetching technology of the streaming media programs of the node server, proposes the load schedule algorithm based on the threshold value of response delay and the caching situation of requesting streaming program at the node server, and takes the simulated test to it.
出处
《计算机与现代化》
2013年第5期108-111,116,共5页
Computer and Modernization
基金
河南省教育厅自然科学研究计划项目资助(2009A520005)
关键词
流媒体
集群服务器
响应延时预测
多级模糊系统
神经网络
前缀缓存
补丁预取
负载调度
streaming media
cluster server
response times estimation
multi-stage fuzzy system
neural networks
prefix caching
patch prefetching
load schedule
