Performing file prediction with a program-based successor model

Recent increases in CPU performance have surpassed those in hard drives. As a result, disk operations have become more expensive in terms of the number of CPU cycles spent waiting for them to complete. File predic-tion can mitigate this problem by prefetching files into cache before they are accessed. Identifying relationships between individual files plays a key role in successfully performing file prefetching. It is well-known that previ-ous patterns of file references can be used to predict future references. Nevertheless, knowledge about the programs producing the relationships between individual files has rarely been investigated. We present a Program-Based Successor (PBS) model that identifies relationships be-tween files through the names of the programs accessing them. We develop a Program-based Last Successor (PLS) model derived from PBS to do file prediction. Our sim-ulation results show that PLS makes 21 % fewer incorrect predictions and roughly the same number of correct pre-dictions as the Last-Successor (LS) model. We also ex-amine the cache hit ratio achieved by applying PLS to the Least Recently Used (LRU) caching algorithm and show that a cache using PLS and LRU together can perform better than a cache up to 40 times larger using LRU alone. Finally, we argue that because program-based successors are more likely to be used soon, incorrectly prefetched program-based successors are more likely to be used and thus less incorrect than incorrectly prefetched files from non-program-based models