Increasing Predictive Accuracy by Prefetching Multiple Program and User Specific Files

Abstract

Recent increases in CPU performance have outpaced increases in hard drive performance. As a result, disk operations have become more expensive in terms of CPU cycles spent waiting for disk operations to complete. File prediction can mitigate this problem by prefetching files into cache before they are accessed. However, incorrect prediction is to a certain degree both unavoidable and costly.

We present the Program-based and User-based Last Successors (PULnS) file prediction model that identifies relationships between files through the names of the programs and the users accessing them. Our simulation results show that, in the worst case, PULnS makes at least 20% fewer incorrect predictions and roughly the same number of correct predictions as the last-successor model.