Selection of Training Period Based on Two-Window

Web prefetching is an important technique to reduce access latency. Although a number of prefetching methods have been proposed in the open literature, few of them study the length of the training period and most of them take arbitrary value for it. Therefore the prediction models of these approaches may keep a great deal of outdated information which occupies the high space and causes their prediction models low efficiency. Based on supervising the prediction accuracy, we present a two-window algorithm to decide the length of the training period. The large window indicates the training period. The small window tracks the prediction accuracy changing which decides whether older examples in the large window are considered outdated and forgotten. We use real web logs to examine proposed algorithm and the simulation shows that our algorithm can significantly improve the prefetching performance.