浏览全部资源
扫码关注微信
1. 华南理工大学电子与信息学院
2. 汕头大学计算机系
纸质出版日期:2010
移动端阅览
蔡伟鸿, 肖水, 韦岗, 等. 基于选择性马尔可夫模型的缓存预取策略[J]. 通信学报, 2010,31(2):58-66.
CAI Wei-hong1, XIAO Shui2, WEI Gang1, et al. Cache prefetching strategy based on selective Markov model[J]. 2010, 31(2): 58-66.
蔡伟鸿, 肖水, 韦岗, 等. 基于选择性马尔可夫模型的缓存预取策略[J]. 通信学报, 2010,31(2):58-66. DOI:
CAI Wei-hong1, XIAO Shui2, WEI Gang1, et al. Cache prefetching strategy based on selective Markov model[J]. 2010, 31(2): 58-66. DOI:
通过分析研究现有流媒体缓存管理算法和用户的访问行为特征
提出了一种新的基于选择性马尔可夫模型的缓存预取策略。该策略通过序列合并方法对用户访问拖曳行为进行建模
采用状态剪枝优化方法FP
V
like得到选择性马尔可夫模型FPMM
like
并在此之上结合替换算法LRU-2构建出一种流媒体代理服务器缓存预取机制FPVlike
L
RU-2。仿真结果表明
在访问延时降低量方面
FPVlike
RU-2要比FP
RU-2、SP
RU-2、LRU-2分别高出10%、12%、17%
且在最佳的情况下该值能够达到60%以上。
Through analyzing the existing streaming media cache management algorithm and user’s watching behavior characteristics
a new cache prefetching strategy based on selective Markov model was presented.The strategy
by mod-eling the user’s VCR action of choosing the merging sequence method
applied the FP
like method to get the selective Markov model FPMM
like and built a streaming media proxy cache prefetching mechanism FP
like-LRU-2 by com-bining the replacement algorithm LRU-2.The experimental results show that
FP
like-LRU-2 is 10%、12%、17% higher than FP
RU-2
SP
RU-2 and LRU-2 respectively in reducing latency experienced by users
and this value is able to reach over 60% in the ideal situation.
0
浏览量
463
下载量
11
CSCD
关联资源
相关文章
相关作者
相关机构