Using program and user information to improve file prediction performance
2001 IEEE International Symposium on Performance Analysis of Systems and Software. ISPASS.
Correct prediction of file accesses can improve system performance by mitigating the relative speed difference between CPU and disks. This paper discusses Program-based Last Successor (PLS) and presents Program-and Userbased Last Successor (PULS), file prediction algorithms that utilize information about the program and user that access the files. Our simulation results show that PLS makes 21% fewer incorrect predictions and PULS makes 24% fewer incorrect predictions than last-successor with
... ghly the same number of correct predictions that lastsuccessor makes. The cache space wasted on incorrect predictions 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. ), we also need to keep track of the file (say © ), which it has most recently accessed. When accesses the next file (say ) after © , we update the metadata of the © with © , A , and the next time that accesses © on behalf of A © program name, user-successor pairs kept for each file. However, our simulation shows that the vast of majority of files are accessed by six or fewer programs and thus metadata storage is not a problem. The last one is that if a program (say © ) eventually executes another program (say ), the information of is also added to the metadata of © , and it will be predicted accordingly in the future.