SchInFS: A File System Integrating Functions of the Block I/O Scheduler for ZNS SSDs

Emerging Zoned Namespace (ZNS) SSDs divide address space into sequentially written zones and transfer garbage collection (GC) to the host, thereby providing more stable performance, increased capacity, and extended device lifespan. However, the sequential write constraint poses some problems for file system design on ZNS devices, particularly leading to bottlenecks in multi-threaded performance for concurrent write requests. Through comprehensive experiments, we analyze the scalability issues of existing POSIX file systems on NVMe ZNS SSDs and identify the root causes: (1) current file systems generally fail to simultaneously utilize the throughput of multiple zones, and (2) their methods for concurrent writing within a single zone are inefficient. To fully exploit the concurrent performance of ZNS SSDs, we propose SchInFS, a novel multi-head logging ZNS SSD file system that integrates the functions of the block I/O scheduler. Firstly, SchInFS employs a multi-head logging design to leverage the throughput of multiple zones concurrently. Secondly, it provides an independent merge queue for each log, facilitating efficient cross-thread write blocks. Finally, SchInFS uses a Block I/O Submission Controller (BSC) to ensure the timely submission of requests and ordered writing within a single zone. Evaluation on real devices demonstrates the effectiveness of SchInFS, showcasing a substantial improvement in the concurrency performance of ZNS SSDs by up to 71.94% compared to current ZNS SSD file systems.

Authors

Institutions

Publication Details

Published
2024-11-18
DOI
https://doi.org/10.1109/iccd63220.2024.00057
Citations
2
Primary Topic
Advanced Data Storage Technologies
Type
article
Field-Weighted Citation Impact
0.70

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

SchInFS: A File System Integrating Functions of the Block I/O Scheduler for ZNS SSDs

Jianxi Chen, Yekang Zhan, Jintong Zhang, Haichuan Hu
2 citations
Advanced Data Storage Technologies
0.70
article

SchInFS: A File System Integrating Functions of the Block I/O Scheduler for ZNS SSDs

Jianxi Chen, Yekang Zhan, Jintong Zhang, Haichuan Hu
article en
2 citations

Abstract

Emerging Zoned Namespace (ZNS) SSDs divide address space into sequentially written zones and transfer garbage collection (GC) to the host, thereby providing more stable performance, increased capacity, and extended device lifespan. However, the sequential write constraint poses some problems for file system design on ZNS devices, particularly leading to bottlenecks in multi-threaded performance for concurrent write requests. Through comprehensive experiments, we analyze the scalability issues of existing POSIX file systems on NVMe ZNS SSDs and identify the root causes: (1) current file systems generally fail to simultaneously utilize the throughput of multiple zones, and (2) their methods for concurrent writing within a single zone are inefficient. To fully exploit the concurrent performance of ZNS SSDs, we propose SchInFS, a novel multi-head logging ZNS SSD file system that integrates the functions of the block I/O scheduler. Firstly, SchInFS employs a multi-head logging design to leverage the throughput of multiple zones concurrently. Secondly, it provides an independent merge queue for each log, facilitating efficient cross-thread write blocks. Finally, SchInFS uses a Block I/O Submission Controller (BSC) to ensure the timely submission of requests and ordered writing within a single zone. Evaluation on real devices demonstrates the effectiveness of SchInFS, showcasing a substantial improvement in the concurrency performance of ZNS SSDs by up to 71.94% compared to current ZNS SSD file systems.

Wuhan National Laboratory for Optoelectronics (CN), Huazhong University of Science and Technology (CN)
National Natural Science Foundation of China, National Key Research and Development Program of China
Openalex Percentile: Top 25%
Advanced Data Storage Technologies
0.70
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.