A group-based wear-leveling algorithm for large-capacity flash memory storage systems
Proceedings of the 2007 international conference on Compilers, architecture …, 2007•dl.acm.org
Although NAND flash memory has become one of the most popular storage media for
portable devices, it has a serious problem with respect to lifetime. Each block of NAND flash
memory has a limited number of program/erase cycles, usually 10,000-100,000, and data in
a block become unreliable after the limit. For this reason, distributing erase operations
evenly across the whole flash memory media is an important concern in designing flash
memory storage systems. In this paper, we propose a memory-efficient group-based wear …
portable devices, it has a serious problem with respect to lifetime. Each block of NAND flash
memory has a limited number of program/erase cycles, usually 10,000-100,000, and data in
a block become unreliable after the limit. For this reason, distributing erase operations
evenly across the whole flash memory media is an important concern in designing flash
memory storage systems. In this paper, we propose a memory-efficient group-based wear …
Although NAND flash memory has become one of the most popular storage media for portable devices, it has a serious problem with respect to lifetime. Each block of NAND flash memory has a limited number of program/erase cycles, usually 10,000-100,000, and data in a block become unreliable after the limit. For this reason, distributing erase operations evenly across the whole flash memory media is an important concern in designing flash memory storage systems.In this paper, we propose a memory-efficient group-based wear-leveling algorithm. Our group-based algorithm achieves a small memory footprint by grouping several logically sequential blocks and managing only the summary information for each group. We also propose an effective group summary structure and a method to reduce unnecessary wear-leveling operations in order to enhance the wear-leveling performance. The evaluation results show that our group-based algorithm consumes only 8.75% of memory space compared to the previous scheme that manages per-block information, while showing roughly the same wear-leveling performance.
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