Abstract
Demand-based flash translation layer is an efficient page-level flash translation layer, which can effectively reduce the RAM (Random Access Memory) footprint of NAND flash-based storage systems. However, this demand-based flash translation layer does not consider the spatial locality of workloads. In this paper, a new workload-aware page-level flash translation layer is proposed for NAND flash-based storage systems. The proposed flash translation layer maintains three caches in RAM to cache mapping entries, which are the on-demand mapping entry cache, frequent mapping entry cache, and dirty mapping entry cache. Considering both temporal locality and spatial locality of workloads, the on-demand mapping entry cache is designed to store the on-demand mapping entries and sequential mapping entries. Considering the access frequency of workloads, the frequent mapping entry cache is designed to cache the most frequently accessed mapping entries. To decrease the number of updates to translation pages, the dirty mapping entry cache is designed to cache the dirty mapping entries and flush the dirty mapping entries belonging to the same translation page to NAND flash memory in a batch mode. The experimental results show that the proposed flash translation layer performs better than existing page-level flash translation layers.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China under Grant Nos. 61502102, 61402109, and 61502103; Natural Science Foundation of Fujian Province, China under Grant Nos. 2016J05149, 2015J05120, and 2017J01737; Fujian Provincial Key Laboratory of Network Security and Cryptology Research Fund (Fujian Normal University) (No. 15008); and Distinguished Young Scientific Research Talents Plan in Universities of Fujian Province (2017).
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Wang, H., Lin, M., Xiong, J., Lin, L., Xiao, R. (2017). Workload-Aware Page-Level Flash Translation Layer for NAND Flash-Based Storage Systems. In: Sun, X., Chao, HC., You, X., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2017. Lecture Notes in Computer Science(), vol 10603. Springer, Cham. https://doi.org/10.1007/978-3-319-68542-7_49
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DOI: https://doi.org/10.1007/978-3-319-68542-7_49
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