Boosting data access based on predictive caching
Chenhan Liao, Jianguo Zheng · 2011
Storage system behaviors are recorded in trace files. The file system trace monitors the file operations from time to time. We show that once a file is created with a set of attributes, such as name, type, permission mode, owner and owner group, its future access frequency is predictable. A regression-tree-based predictive model is established to predict whether a file will be frequently accessed or not. By consulting with the rules generated from the predictive model over diverse real-system NFS traces, it can predict a newly created file's future access frequency with a sufficient accuracy. We further introduce an evolutionary storage system, which the predicted frequency information could be used to decide what files to keep in a flash memory. The trace-driven experimental results indicate that the performance speedup due to the prediction-enabled optimization is 2-4.