Title
Improving in-memory file system reading performance by fine-grained user-space cache mechanisms
Abstract
Nowadays, as the memory capacity of servers become larger and larger, distributed in-memory file systems, which enable applications to interact with data at fast speed, have been widely used. However, the existing distributed in-memory file systems still face the problem of low data access performance in small data reading, which seriously reduce their usefulness in many important big data scenarios. In this paper, we analyze the factors that affect the performance of reading in-memory files and propose a two-layer user space cache management mechanism: in the first layer, we cache data packet references to reduce frequent page fault interruptions (packet-level cache); in the second layer, we cache and manage small file data units to avoid redundant inter-process communications (object-level cache). We further design a fine-grained caching model based on the submodular function optimization theory, for efficiently managing the variable-length cache units with partially overlapping fragments on the client side. Experimental results on synthetic and real-world workloads show that compared with the existing cutting-edge systems, the first level cache can double the reading performance on average, and the second level cache can improve random reading performance by more than 4 times. Our caching strategies also outperform the cutting-edge cache algorithms over 20% on hit ratio. Furthermore, the proposed client-side caching framework idea has been adopted by the Alluxio open source community, which shows the practical benefits of this work.
Year
DOI
Venue
2021
10.1016/j.sysarc.2021.101994
Journal of Systems Architecture
Keywords
DocType
Volume
Distributed file system,Cache policy,Submodular optimization,Distributed system
Journal
115
ISSN
Citations 
PageRank 
1383-7621
0
0.34
References 
Authors
40
7
Name
Order
Citations
PageRank
Rong Gu111017.77
Chongjie Li200.34
Dai Haipeng341955.44
Yili Luo400.34
Xu Xiaolong542464.23
Shaohua Wan638248.34
Yihua Huang786.61