StorStack: A full-stack design for in-storage file systems

Due to the increasingly significant cost of data movement, In-storage Computing has attracted considerable attention in academia. While most In-storage Computing works allow direct data processing, these methods do not completely eliminate the participation of the CPU during file access, and data still needs to be moved from the file system into memory for processing. Even though there are attempts to put file systems into storage devices to solve this problem, the performance of the system is not ideal when facing high latency storage devices due to bypassing the kernel and lacking page cache.

To address the above issues, we propose StorStack, a full-stack, highly configurable in-storage file system framework, and simulator that facilitates architecture and system-level researches. By offloading the file system into the storage device, the file system can be closer to the data, reducing the overhead of data movements. Meanwhile, it also avoids kernel traps and reduces communication overhead. More importantly, this design enables In-storage Computing applications to completely eliminate CPU participation. StorStack also designs the user-level cache to maintain performance when storage device access latency is high. To study performance, we implement a StorStack prototype and evaluate it under various benchmarks on QEMU and Linux. The results show that StorStack achieves up to 7x performance improvement with direct access and 5.2x with cache.