An Optimal Model of Web Cache Based on Improved K-Means Algorithm

Abstract

Replacement algorithm optimization is the core of cache model research. On the basis of the cache replacement model RFS, through long-term observation and analysis to the real network logs, find that the fluctuation of the access interval change rate is more valuable in predicting the new objects arrival. Therefore, in this paper, we first get the access heat level through clustering the access interval change rate with the improved K-means clustering algorithm; and then establish HSF optimal web cache model with the access heat level which named H, web object size which named S and web object freshness which named F. The replacement strategy of HSF model’s is: First, replace the lowest heat level of the web object; replace the biggest size one, if H is the same; replace The lowest freshness one if H and S are the same. The simulation shows that the HSF model had the better hit rate and the byte hit rate, and the lower the access delay than the RFS.