The enormous potential of locality based strategies like caching and prefetching to improve web performance motivates us to propose a novel global framework for performance evaluation in scenarios where different parts of the web architecture interact. Unlike existing proposals our approach is a fast and flexible tool that allows to represent faithfully the behaviour of each element of the architecture in order to study, reproduce, evaluate and design web strategies to decrease the user´s perceived latency when surfing the web.

Prefetching is an interesting technique for improving web performance by reducing the user-perceived latency when surfing the web. Nevertheless, due to its speculative nature, prefetching can increase the network traffic and the server load. This could negatively affect the overall system performance and decrease the quality of service. To minimize and maintain under control these adverse effects, in this paper we propose an intelligent prefetching mechanism that dynamically adjusts the aggressiveness of the prefetching algorithm at the server side. To this end, we also propose a traffic estimation model that permits to accurately calculate, in the server side, the extra load and traffic generated by the prefetching. The performance evaluation study shows that our proposal effectively regulates the adverse effects of web prefetching without affecting its benefits.