Practical prefetching via data compression
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Long term distributed file reference tracing: implementation and experience
Software—Practice & Experience
Optimal prefetching via data compression
Journal of the ACM (JACM)
Input/output access pattern classification using hidden Markov models
Proceedings of the fifth workshop on I/O in parallel and distributed systems
Design and Implementation of a Predictive File Prefetching Algorithm
Proceedings of the General Track: 2002 USENIX Annual Technical Conference
The Case for Efficient File Access Pattern Modeling
HOTOS '99 Proceedings of the The Seventh Workshop on Hot Topics in Operating Systems
Group-Based Management of Distributed File Caches
ICDCS '02 Proceedings of the 22 nd International Conference on Distributed Computing Systems (ICDCS'02)
Unbounded length contexts for PPM
DCC '95 Proceedings of the Conference on Data Compression
File access prediction with adjustable accuracy
PCC '02 Proceedings of the Performance, Computing, and Communications Conference, 2002. on 21st IEEE International
Reducing file system latency using a predictive approach
USTC'94 Proceedings of the USENIX Summer 1994 Technical Conference on USENIX Summer 1994 Technical Conference - Volume 1
Predicting file system actions from prior events
ATEC '96 Proceedings of the 1996 annual conference on USENIX Annual Technical Conference
An analytical approach to file prefetching
ATEC '97 Proceedings of the annual conference on USENIX Annual Technical Conference
The Design of the SEER Predictive Caching System
WMCSA '94 Proceedings of the 1994 First Workshop on Mobile Computing Systems and Applications
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In modern file systems, I/O latency is still major bottleneck of performance and predictive file prefetching is one of promising approaches that can enhance I/O performance of file system. To utilize predictive file prefetching to file system, there should be a file access pattern prediction model that can predict future file access. Partitioned Context Model(PCM) [2] is known as one of the most accurate file access pattern prediction models[3]. In order to predict longer sequence, the order of PCM must be increased. However, the prediction accuracy of PCM decreases when PCM is in high order. Careful analysis reveals that the pattern replacement algorithm in the PCM is the major cause in decay of the prediction accuracy. The pattern replacement algorithm destroys file access patterns without successful training of newly occurred file access patterns. We proposed the constrained pattern replacement algorithm to overcome this adverse effect by revising replacement condition. The simulation results using the DFS Trace system trace[13] show that the proposed algorithm improves prediction accuracy without any extra cost by 3.5% compared to traditional pattern replacement algorithm of PCM(about 40% of the accuracy bound of 7%).