Intelligent Client-side Web Caching Scheme Based on Least recently Used Algorithm and Neuro-Fuzzy

Abstract. Web caching is a well-known strategy for improving performance of Web-based system by keeping web objects that are likely to be used in the near fu-ture close to the client. Most of the current Web browsers still employ traditional caching policies that are not efficient in web caching. This research proposes a splitting client-side web cache to two caches, short-term cache and long-term cache. Primarily, a web object is stored in short-term cache, and the web objects that are visited more than the pre-specified threshold value will be moved to long-term cache, while other objects are removed by Least Recently Used(LRU) algo-rithm as short-term cache is full. More significantly, when the long-term cache saturates, the trained neuro-fuzzy system is employed in classifying each object stored in long-term cache into cacheable or uncacheable object. The old uncache-able objects are candidate for removing from the long-term cache. By implement-ing this mechanism, the cache pollution can be mitigated and the cache space can be utilized effectively. Experimental results have revealed that the proposed ap-proach has better performance compared to the most common caching policies and has improved the performance of client-side caching substantially