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Reinforcement learning-driven address mapping and caching for flash-based remote sensing image processing.
- Source :
-
Journal of Systems Architecture . Sep2019, Vol. 98, p374-387. 14p. - Publication Year :
- 2019
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 13837621
- Volume :
- 98
- Database :
- Academic Search Index
- Journal :
- Journal of Systems Architecture
- Publication Type :
- Academic Journal
- Accession number :
- 138270779
- Full Text :
- https://doi.org/10.1016/j.sysarc.2019.02.007