Using Multiple Predictors to Improve the Accuracy of File Access Predictions
MSS '03 Proceedings of the 20 th IEEE/11 th NASA Goddard Conference on Mass Storage Systems and Technologies (MSS'03)
A stochastic approach to file access prediction
SNAPI '03 Proceedings of the international workshop on Storage network architecture and parallel I/Os
A performance analysis of a cache-based file prediction protocol for mobile file systems
Proceedings of the ACM international workshop on Performance monitoring, measurement, and evaluation of heterogeneous wireless and wired networks
TOCTTOU vulnerabilities in UNIX-style file systems: an anatomical study
FAST'05 Proceedings of the 4th conference on USENIX Conference on File and Storage Technologies - Volume 4
Improving the performance of the web proxy server through group prefetching
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
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Abstract: Recent increases in CPU performance have surpassed those in hard drives. As a result, disk operations have become more expensive in terms of the number of CPU cycles spent waiting for them to complete. File prediction can mitigate this problem by prefetching files into cache before they are accessed. Identifying relationships between individual files plays a key role in successfully performing file prefetching. It is well-known that previous pat-terns of file references can be used to predict future references. Nevertheless, knowledge about the programs producing the relationships between individual files has rarely been investigated. We present a Program-Based Successor (PBS) model that identifies relationships between files through the names of the programs accessing them. We develop a Program-based Last Successor (PLS) model derived from PBS to do file prediction. Our simulation results show that PLS makes 21% fewer incorrect predictions and roughly the same number of correct predictions as the Last-Successor (LS) model. We also examine the cache hit ratio achieved by applying PLS to the Least Recently Used (LRU) caching algorithm and show that a cache using PLS and LRU together can perform better than a cache up to 40 times larger using LRU alone. Finally, we argue that because program-based successors are more likely to be used soon, incorrectly prefetched program-based successors are more likely to be used and thus less incorrect than incorrectly prefetched files from non-program-based models.