Paper Info
Title
Multigranularity Space Management Scheme for Accelerating the Write Performance of In-Memory File Systems
Abstract
Emerging nonvolatile memory (NVM) techniques, such as phase change memory (PCM), spin-transfer torque magnetic random access memory (STT-MRAM), and resistive random-access memory, are promising for high-performance data process by reserving data in the memory hierarchy. Many persistent memory file systems are tailored to achieve high performance by exploring the advanced features of the NVM and the hardware memory management unit (MMU) in the CPU. However, with the efficient storage device and the hardware acceleration, the write routines in persistent memory file systems pose considerable overhead since repeatedly allocating free blocks and constructing the file mapping structure are time consuming. In this article, we propose a new multigranularity space management scheme (MSMS) to accelerate the write performance. The MSMS employs multigranularity structured blocks whose mapping structure is proactively constructed to slash the overhead of allocating new space and constructing the file mapping structure. Moreover, we present efficiently dedicated space allocation algorithms for different write modes. For append write, we present a file-sizeand buffer-sizebased allocation (FBA) algorithm to efficiently allocate the appropriate blocks. And for copy-on-write, we present an updating data and offset-based allocation algorithm to preferentially allocate structured huge blocks for reducing the overhead of invoking allocation routines. Based on the new design, we have implemented the MSMS for SIMFS in the Linux kernel. Experimental results show that the MSMS significantly reduces the times of invoking allocation routines. The average append write and copy-on-write performance with the MSMS improve by 16.34% and 7.51%, respectively.
Year
DOI
Venue
2020
10.1109/JSYST.2020.2975673
IEEE Systems Journal
Keywords
DocType
Volume
Allocation algorithm,file systems,space management,write performance
Journal
14
Issue
ISSN
Citations 
4
1932-8184
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
ting wu13916.80
Kai Liu200.34
Chunhua Xiao308.45
Bingyi Liu4274.49
Qingfeng Zhuge575160.37
Edwin H.-M. Sha6131897.35