A New Model for Web Pre-fetching Using Popularity-Based Prediction

Abstract

Due to the fast development of internet services and a huge amount of network traffic, it is becoming an essential issue to reduce World Wide Web user-perceived latency. Although web performance is improved by caching, the benefit of caches is limited. To further reduce the retrieval latency, web prefetching becomes an attractive solution to this problem. In this work, we introduce a simple and transparent popularity-based prefetching algorithm which combines both the top 10 and next-n prefetching approaches. In addition to using access-frequency as the criteria for prefetching, we also use the time of access of web documents to generate the top 10 list. This approach of using accessfrequency and time of access is known as the GDSP approach, which has been used in cache management. Instead of generating next-n list for all the documents accessed by the users, we log the next-n documents for the top 10 documents only, thus reducing complexity and overhead. The results obtained from algorithm, in terms of hit rate and prefetching effectiveness show the efficacy of our proposed algorithm as compared to other approaches.