Web Browsing Optimization: a Prefetching System Based on Prediction History

Web browsing response times are affected by the heterogeneous nature of Internet: links with disparate bandwidth, servers and routers with diverse specifications and network segments with particular traffic shaping policies. Also, on a daily basis, several new information systems get connected to the Internet consequently increasing traffic. Aiming to reduce response times, prefetching systems predict which objects will be requested by users based on predictive models. This way, predicted objects are requested and stored in cache in advance. However, literature shows that current prefetching systems do not contemplate how effective the predictions are. This paper presents a feedback based prefetching system. The proposed approach considers previous predictions hits and misses to update the predictive model thus enhancing the precision of predictions. The experimental study shows an improvement on users' response time obtained by the proposed system.