Intelligent client-side web caching scheme based on least recently used algorithm and neuro-fuzzy system

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 future 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) algorithm 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 uncacheable objects are candidate for removing from the long-term cache. By implementing this mechanism, the cache pollution can be mitigated and the cache space can be utilized effectively. Experimental results have revealed that the proposed approach has better performance compared to the most common caching policies and has improved the performance of client-side caching substantially