التفاصيل البيبلوغرافية
| العنوان: |
Efficient Multi-Grained Wear Leveling for Inodes of Persistent Memory File Systems. |
| المؤلفون: |
Chaoshu Yang, Duo Liu, Runyu Zhang, Xianzhang Chen, Shun Nie, Fengshun Wang, Qingfeng Zhuge, Sha, Edwin H.-M. |
| المصدر: |
DAC: Annual ACM/IEEE Design Automation Conference; 2020, Issue 57, p572-577, 6p |
| مصطلحات موضوعية: |
ELECTRONIC file management, LINUX operating systems, DATA analysis, PROBLEM solving, ACCURACY |
| مستخلص: |
Existing persistent memory file systems usually store inodes in fixed locations, which ignores the external and internal imbalanced wears of inodes on the persistent memory (PM). Therefore, the PM for storing inodes can be easily damaged. Existing solutions achieve low accuracy of wear-leveling with high-overhead data migrations. In this paper, we propose a Lightweight and Multi-grained Wear-leveling Mechanism, called LMWM, to solve these problems. We implement the proposed LMWM in Linux kernel based on NOVA, a typical persistent memory file system. Compared with MARCH, the state-of-the-art wear-leveling mechanism for inode table, experimental results show that LMWM can improve 2.5x lifetime of PM and 1.12x performance, respectively. [ABSTRACT FROM AUTHOR] |
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