Reinforcement Learning-driven Address Mapping and Caching for Flash-based Remote Sensing Image Processing
Flash, FTL, Reinforcement learning, Remote sensing
College of Natual Science and Mathematics, Geography and the Environment
Flash memory is featured with salient advantages over conventional hard disks for massive data storage and efficient on-board data processing. A flash translation layer (FTL) is a critical component for flash-based storage devices to handle particular technical constraints of flash. It is desirable to use flash memory for the storage of massive remote sensing images and support on-board remote sensing data processing applications, which typically require high I/O performance and hence call for advanced FTL design and implementations. In this paper, we introduce our efforts in developing a reinforcement learning driven page-level mapping and caching scheme (named Q-FTL) that is adaptive and responsive to ever-changing I/O streams of on-board remote sensing image processing operations. The adaptability and responsiveness are achieved by the separation of large and small I/O requests, an integrated weighting scheme to measure access costs of cached translation pages, and a reinforcement learning driven cache replacement algorithm. We demonstrate the efficiency of the proposed approach using actual I/O traces generated from on-board remote sensing image processing applications. Experimental results show that Q-FTL improves over several current state-of-the-art FTLs by a large margin and even achieves competitive performance close to an idealized pure page mapping FTL in some cases.
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Zhang, Tong, et al. “Reinforcement Learning-Driven Address Mapping and Caching for Flash-Based Remote Sensing Image Processing.” Journal of Systems Architecture, vol. 98, 2019, pp. 374–387. doi: 10.1016/j.sysarc.2019.02.007.