Fast and Low Overhead Metadata Operations for NVM-Based File System Using Slotted Paging

日志文件系统 元数据 计算机科学 文件系统 操作系统 架空(工程) 寻呼 版本控制文件系统 计算机文件 文件系统碎片 数据库
作者
Fangzhu Lin,Chunhua Xiao,Weichen Liu,Lin Wu,Chen Shi,Kun Ning
出处
期刊:IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems [Institute of Electrical and Electronics Engineers]
卷期号:41 (11): 4481-4491 被引量:1
标识
DOI:10.1109/tcad.2022.3202436
摘要

Existing nonvolatile memory (NVM)-based file systems can fully leverage the characteristics of NVM to obtain better performance than traditional disk-based file systems. It has the potential capacity to efficiently manage metadata and perform fast metadata operations. However, most NVM-based file systems mainly focus on managing file metadata (inode), while pay little attention to directory metadata (dentry), which also has a noticeable impact on the file system performance. Besides, the traditional journaling technique that guarantees metadata consistency may not yield satisfactory performance on NVM-based file systems. To solve these problems, in this article we propose a fast and low overhead metadata operation mechanism, called FLOMO. It first adopts a novel slotted-paging structure in NVM to reorganize dentry for efficiently performing dentry operations, and utilizes the red–black tree in DRAM to accelerate dentry lookup and the search process of dentry deletion. Moreover, FLOMO presents a selective journaling scheme for metadata updates, which partially logs the changes related to dentry in the proposed slotted page, thereby, mitigating the redundant journaling overhead. To verify FLOMO, we implement it in a typical NVM-based file system, the persistent memory file system (PMFS). Experimental results show that FLOMO accelerates the metadata operations in PMFS by 34.4% $\sim 59$ %, and notably reduces the journaling overhead for metadata, shortening the latency by 59% on average. For real-world applications, FLOMO has higher throughput compared with PMFS, PMFS without journal, and NOVA, achieving up to $2.1\times $ , $1.1\times $ , and $1.3\times $ performance improvement, respectively.
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