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Jianxing Zhao

dblp:131/5592 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2019
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Storage systems · 61% Distributed systems · 30% Cloud and datacenter computing · 9%
Databases, data mining, and information retrieval
1 paper
Data integration and cleaning · 100%

Topics — the 4 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Data integration and cleaning
metadata management
0.412019
CFS: A Distributed File System for Large Scale Container Platforms · SIGMOD Conference 2019
Storage systems › file systems
distributed file system
0.412019
CFS: A Distributed File System for Large Scale Container Platforms · SIGMOD Conference 2019
Distributed systems › replication
replica control
0.412019
CFS: A Distributed File System for Large Scale Container Platforms · SIGMOD Conference 2019
Storage systems
storage reliability
0.412019
CFS: A Distributed File System for Large Scale Container Platforms · SIGMOD Conference 2019

Methods — techniques the papers use, named apart from their topics

replication · 0.8POSIX-compliant APIs · 0.8
YearPublicationVenuePosition
2019 CFS: A Distributed File System for Large Scale Container Platforms
abstract
We propose CFS, a distributed file system for large scale container platforms. CFS supports both sequential and random file accesses with optimized storage for both large files and small files, and adopts different replication protocols for different write scenarios to improve the replication performance. It employs a metadata subsystem to store and distribute the file metadata across different storage nodes based on the memory usage. This metadata placement strategy avoids the need of data rebalancing during capacity expansion. CFS also provides POSIX-compliant APIs with relaxed semantics and metadata atomicity to improve the system performance. We performed a comprehensive comparison with Ceph, a widely-used distributed file system on container platforms. Our experimental results show that, in testing 7 commonly used metadata operations, CFS gives around 3 times performance boost on average. In addition, CFS exhibits better random-read/write performance in highly concurrent environments with multiple clients and processes.
Weilong Guo, Shuoran Liu, Tianpeng Li, Mofei Zhang, Jianxing Zhao, Hongyin Zhu, Zhengyi Zhu
SIGMOD Conference8