The Log-structured File System (LFS) transforms random writes to a huge sequential one to provide superior write performance on storage devices. However, LFS inherently suffers from overhead incurred by cleaning segments. Specifically, when file system utilization is high and the system is busy, write performance of LFS degenerates significantly due to high cleaning cost. Also, in the newer flash memory based SSD storage devices, cleaning leads to reduced SSD lifetime as it incurs more writes. In this paper, we propose an enhancement to the original LFS to alleviate the performance degeneration due to cleaning when the system is busy. The new scheme, which we call Slack Space Recycling (SSR), allows LFS to delay on-demand cleaning during busy hours such that cleaning may be done when the load is much lighter. Specifically, it writes modified data directly to invalid areas (slack space) of used segments instead of cleaning on-demand, pushing back cleaning for later. SSR also has the added benefit of increasing the lifetime of the now popular SSD storage devices. We implement the new SSR-LFS file system in Linux and perform a large set of experiments. The results of these experiments show that the SSR scheme significantly improves performance of LFS for a wide range of storage utilization settings and that the lifetime of SSDs is extended considerably.

This letter introduces a buffer management issue in designing SSDs for log-structured file systems (LFSs). We implemented a novel trace-driven SSD simulator in SystemC language, and simulated several SSD architectures with the NILFS2 trace. From the results, we give two major considerations related to the buffer management as follows. (1) The write buffer is used as a buffer not a cache, since all write requests are sequential in NILFS2. (2) For better performance, the main architectural factor is the bus bandwidth, but 332 MHz is enough. Instead, the read buffer makes a key role in performance improvement while caching data. To enhance SSDs, accordingly, it is an effective way to make efficient read buffer management policies, and one of the examples is tracking the valid data zone in NILFS2, which can increase the data hit ratio in read buffers significantly.