Web cache intelligent replacement strategy combined with GDSF and SVM network re-accessed probability prediction

Web caching is used to solve the problem of network access delays and network congestion. The intelligent cache replacement strategy directly affects the cache hit rate. This paper proposed a web cache replacement strategy combining greedy dual size frequency (GDSF) algorithm and support vector machine (SVM) re-accessed probability prediction. In the traditional GDSF method, a new objective function is constructed by considering the network object type and object re-accessed probability. The object re-accessed probability is predicted by learning the historical access data through SVM classifier. The simulation results show that compared with the traditional LRU and GDSF schemes, the proposed strategy has a higher request hit rate and byte hit ratio. When the cache size is 16%, the HR and BHR values reached 0.623 and 0.522, respectively.