DDG: An Efficient Prefetching Algorithm for Current Web Generation

Web prefetching is one of the techniques proposed to reduce user's perceived latencies in the World Wide Web. The spatial locality shown by user's accesses makes it possible to predict future accesses based on the previous ones. A prefetching engine uses these predictions to prefetch the Web objects before the user demands them. The existing prediction algorithms achieved an acceptable performance when they were proposed but the high increase in the amount of embedded objects per page has reduced their effectiveness in the current Web. In this paper we show that most of the predictions made by the existing algorithms are useless to reduce the user's perceived latency because these algorithms do not take into account how current Web pages are structured, i.e., an HTML object with several embedded objects. Thus, they predict the accesses to the embedded objects in an HTML after reading the HTML itself. For this reason, the prediction advance is not enough to prefetch the objects and therefore there is no latency reduction. As a result of a wide analysis of the behaviour of the most commonly used algorithms, in this paper we present the DDG algorithm that distinguishes between container objects (HTML) and embedded objects to create a new prediction model according to the structure of the current Web. Results show that, for the same amount of extra requests to the server, DDG always outperforms the existing algorithms by reducing the perceived latency between 15% and 150% more without increasing the computing complexity