LOFFS: A Low-Overhead File System for Large Flash Memory on Embedded Devices

Runyu Zhang, Duo Liu, Xianzhang Chen, Xiongxiong She, Chaoshu Yang, Yujuan Tan, Zhaoyan Shen, Zili Shao

Abstract: Emerging applications like machine learning in embedded devices (e.g., satellite and vehicles) require huge storage space, which recently stimulates the widespread deployment of large-capacity flash memory in IoT devices. However, existing embedded file systems fall short in managing large-capacity storage efficiently for excessive memory consumption and poor booting performance. In this paper, we propose a novel embedded file system, LOFFS, to tackle the above issues and manage large-capacity NAND flash on resource-limited embedded devices. We redesign the space management mechanisms and construct hybrid file structures to achieve high performance with minimum resource occupation. We have implemented LOFFS in Linux, and the experimental results show that LOFFS outperforms YAFFS by 55.8% on average with orders of magnitude reductions on memory footprint.