Mining-based File Caching in a Hybrid Storage System

In this work, we propose a new mining-based file caching scheme for a hybrid storage disk system. In particular, we focus our efforts on reducing the latency of launching applications. The proposed scheme identifies correlated file accesses in a file access sequence via sequential pattern mining algorithm. Our scheme caches correlated files together to maximize the caching efficiency. The correlated files are extracted from the access patterns through the proposed mining scheme, which consists of three steps: frequent pattern based file extraction, cluster moving gap based file sort, and frequency and size based file prioritization. The extracted correlated files are relocated to an SSD during idle time. DiskSim and NANDSim are used to evaluate the proposed scheme, called Informed Mining. The proposed scheme is compared with a disk only scheme and five other mining based file relocation schemes: Mining based file relocation scheme (Miner), minimum distance based file relocation scheme (Min_Dist), frequency-based relocation scheme (Fre), size-based relocation scheme (Size), and one that relocates files with highest value of (file size * file access number) first to the SSD (Fr*Sz). From the simulation based experiment, launch time is reduced by about 50% using only 10% of sum of all file sizes accessed during a launch of an application.