A PPM Prediction Model Based on Web Objects' Popularity
A PPM Prediction Model Based on Web Objects' Popularity
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DOI：
10.1007/11540007_15
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发表时间：
2005-08
期刊：
影响因子：
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通讯作者：
Lei Shi;Zhimin Gu;Yunxia Pei;Lin Wei
中科院分区：
文献类型：
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作者：
Lei Shi;Zhimin Gu;Yunxia Pei;Lin Wei
Web预取技术是减少Web访问延迟和提高服务质量的主要解决方案之一。本文利用Zipf第一定律和Zipf第二定律对Web对象的流行度进行建模，其中Zipf第一定律对高频Web对象进行建模，Zipf第二定律对低频Web对象进行建模，提出了一种基于Web对象流行度的PPM预测模型用于Web预取。利用真实服务器日志对该模型进行了性能评估。跟踪驱动的仿真结果表明，该模型不仅易于实现，而且能够以相对较低的存储复杂度和网络流量为代价实现较高的预测精度。
Web prefetching technique is one of the primary solutions used to reduce Web access latency and improve the quality of service. This paper makes use of Zipf’s 1st law and Zipf’s 2nd law to model the Web objects’ popularity, where Zipf’s 1st law is employed to model the high frequency Web objects and 2nd law for the low frequency Web objects, and proposes a PPM prediction model based on Web objects’ popularity for Web prefetching. A performance evaluation of the model is presented using real server logs. Trace-driven simulation results show that not only the model is easily to be implemented, but also can achieve a high prediction precision at the cost of relative low storage complexity and network traffic.