Using program and user information to improve file prediction performance

Abstract

Correct prediction of file accesses can improve system pe formance by mitigating the relative speed difference between CPU and disks. This paper discusses Program-based Last Successor (PLS) and presents Program- and Userbased Lust Successor (PULS), file prediction algorithms that utilize information about the program and user that access the jles. Our simulation results show that PLS makes 21% fewer incorrect predictions and PULS makes 24% fewer incorrect predictions than last-successor with roughly the same number of correct predictions that lastsuccessor makes. The cache space wasted on incorrectpredictions can be reduced accordingly. We also show that a cache using the Least Recently Used (LRU) caching algorithm can perform better when the PULS is applied. In some cases, a cache using LRU and either PLS or PULS performs better than a cache up to 40 times larger using LRU alone.