Paper Info
Title
File Aware Wear Leveling for PCM-based Mobile Consumer Electronics
Abstract
Phase Change Memory (PCM) is considered as one of the most popular candidates to replace flash memory in mobile consumer systems. PCM has many superior performance characteristics, including non-volatility, byte-addressability, low access latency and power consumption. However, it also suffers from finite program counts like flash memory. Prior researches used PCM as a black box, and implemented the wear leveling schemes in device controller, which failed to utilize file attributes in host side and result in poor efficiency of wear evenness. In this paper, we propose a file aware wear leveling algorithm (called FAWL) for PCM-based storage system in mobile consumer electronics. FAWL is designed in the host side, which combines file attributes and statistical information of PCM. It exploits rich attributes of files to divide files into different categories and distribute them in suitable pages to avoid extra swap overhead. In addition, by utilizing an adjust management in FAWL, the wear imbalance can be greatly mitigated. Experimental results show that FAWL effectively improves the lifetime of PCM compared with existing wear leveling algorithms, including random swapping, start-gap and segment swapping.
Year
DOI
Venue
2017
10.1109/HPCC-SmartCity-DSS.2017.72
2017 IEEE 19th International Conference on High Performance Computing and Communications; IEEE 15th International Conference on Smart City; IEEE 3rd International Conference on Data Science and Systems (HPCC/SmartCity/DSS)
Keywords
Field
DocType
host-based wear leveling,file system,file attribute,space organization.
Black box (phreaking),Phase-change memory,Control theory,File system,Flash memory,Wear leveling,Computer science,Computer data storage,Real-time computing,File attribute,Embedded system
Conference
ISBN
Citations 
PageRank 
978-1-5386-2589-7
0
0.34
References 
Authors
14
6
Name
Order
Citations
PageRank
Zheng Zhang102.37
Dan Feng21845188.16
Zhipeng Tan33310.18
Jianxi Chen415310.60
Wei Zhou563.48
Laurence T. Yang66870682.61