摘要
为了提高web缓存的性能,提出了一种基于多Markov链预测模型的Web缓存替换算法PGDSF-AI.首先将Web中具有不同浏览特征的用户分为多类,为每一类用户建立类Markov链,进一步建立多Markov链预测模型.然后利用该模型对当前的用户请求预测,进而组成预测对象集.当缓存空间不足时,选取键值最小且不在预测对象集中的对象替换.通过估算对象的平均间隔时间,避免缓存大量保留长时间没有访问的对象.实验结果表明,提出的算法有较好的性能.
To improve the performance of web cache , a new cache replacement algorithm named PGDSF-AI is proposed .The Algorithm is based on multi-Markov chains prediction model and characteristics of user browsing is used .Firstly ,the Web users are classified according to browsing characteristics and a classified-Markov is built for a classified users ,and then multi-Markov chains prediction model is constructed .Then ,the prediction model is forecasted based on user access sequence and the target set of prediction is formed .When the cache has not enough space to accommodate new request ,replace the key which is the smallest and does not belong to the target set of prediction .The average interval is used to avoid objects long time no access retaining in the cache .Experimental results have showed that the proposed algorithm had good performance .
出处
《微电子学与计算机》
CSCD
北大核心
2014年第5期36-40,共5页
Microelectronics & Computer
基金
国家自然科学基金资助项目(71061008)
