Caching Files with a Program-based Last n Successors Model

Recent increases in CPU performance have outpaced in-creases in hard drive performance. As a result, disk opera-tions 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 les into cache before they are accessed. However, incorrect pre-diction is to a certain degree both unavoidable and costly. We present the Program-based Last N Successors (PLNS) le prediction model that identies relationships between les 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 per-form as well as a cache up to 40 times larger using LRU alone.