Wenbao Jia

Wenbao Jia

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Wenbao Jia

State Key Lab of Processors, Institute of Computing Technology, Chinese Academy of Sciences

Research Focus & Metrics

Academic Career Length

13Years (2014-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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1 July 2021 in Journal of Hazardous Materials
Verified
Journal Article
Closed access
YLYongsheng Ling
Yongsheng Ling
QYQi Yue
Qi Yue
THTian Huang
Tian Huang
QSQing Shan
Qing Shan
DHDaqian Hei
Daqian Hei
WJWenbao Jia
Wenbao Jia


5 April 2014 in Korean Journal of Chemical Engineering
Verified
Journal Article
Closed access
PDF file
WJWenbao Jia
Wenbao Jia
SLSongsheng Lu
Songsheng Lu

3 November 2025 in ACM Transactions on Storage
Verified
Journal Article
Open access
WJWenqing Jia
Wenqing Jia
DJDejun Jiang
Dejun Jiang
JXJin Xiong
Jin Xiong
We present AugeFS , a scalable userspace log-structured file system for modern SSDs. AugeFS re-architects the file system stack to address three critical challenges: inefficient control plane, limited metadata scalability, and underutilized device bandwidth. First, we propose a shared and protected address space within the userspace of accessing applications to run AugeFS , which enables the high-performance data plane and efficient control plane. Second, we design a scalable LSM-tree based key-value store called MetaDB to organize small-sized metadata in AugeFS . To improve the metadata scalability, MetaDB employs parallel request processing to reduce thread synchronization overhead and fine-grained parallel write-ahead log to eliminate false sharing in metadata persistence. Finally, AugeFS distributes files into different domains. To reduce contention, we maintain space management metadata for each domain independently, which helps scale data performance and improve the device IO utilization. Moreover, AugeFS designs an asynchronous IO stack for fsync to reduce the latency of synchronous writes. The evaluation results show that AugeFS significantly improves both metadata scalability and data scalability.