Boosting data access based on predictive caching

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.