An adaptive network prefetch scheme

In this paper, we present an adaptive prefetch scheme for network use, in which we download files that will very likely be requested in the near future, based on the user access history and the network conditions. Our prefetch scheme consists of two parts: a prediction module and a threshold module. In the prediction module, we estimate the probability with which each file will be requested in the near future. In the threshold module, we compute the prefetch threshold for each related server, the idea being that the access probability is compared to the prefetch threshold. An important contribution of this paper is that we derive a formula for the prefetch threshold to determine its value dynamically based on system load, capacity, and the cost of time and system resources to the user. We also show that by prefetching those files whose access probability is greater than or equal to its server's prefetch threshold, a lower average cost can always be achieved. As an example, we present a prediction algorithm for web browsing. Simulations of this prediction algorithm show that, by using access information from the client, we can achieve high successful prediction rates, while using that from the server generally results in more hits.