Caching Files with a Program-based Last N Successors Model

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 pre- diction 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 Last N Successors (PLNS) file prediction model that identifies relationships between files through the names of the programs accessing them. Our simulation results show that PLNS makes at least 21.11% fewer incorrect predictions and roughly the same number of correct predictions as the last-successor model. We also examine the cache hit ratio of applying PLNS to the Least Recently Used (LRU) caching algorithm and show that a cache using PLNS and LRU together can perform as well as a cache up to 40 times larger using LRU alone