基于选择性马尔可夫模型的缓存预取策略

Cache prefetching strategy based on selective Markov model

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_Vlike得到选择性马尔可夫模型FPMM_Vlike,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_LRU-2。仿真结果表明,在访问延时降低量方面,FPVlike_LRU-2要比FP_LRU-2、SP_LRU-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_Vlike method to get the selective Markov model FPMM_Vlike and built a streaming media proxy cache prefetching mechanism FP_Vlike-LRU-2 by com- bining the replacement algorithm LRU-2.The experimental results show that, FP_Vlike-LRU-2 is 10%, 12%, 17% higher than FP_LRU-2, SP_LRU-2 and LRU-2 respectively in reducing latency experienced by users, and this value is able to reach over 60% in the ideal situation.