The State of the Art of Metadata Managements in Large-Scale Distributed File Systems — Scalability, Performance and Availability

Author:

Dai Hao1ORCID,Wang Yang1ORCID,Kent Kenneth B.2ORCID,Zeng Lingfang3ORCID,Xu Chengzhong4ORCID

Affiliation:

1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

2. Faculty of Computer Science, University of New Brunswick, Fredericton, NB, Canada

3. Zhejiang Lab, ZJ Lab-Enflame Joint Innovation Research Center, Hangzhou, Zhejiang, China

4. Faculty of Science and Technology, University of Macau, Macau, China

Funder

Third Xinjiang Scientific Expedition Program

National Natural Science Foundation of China

Zhejiang provincial

Center-initiated Research Project of Zhejiang Lab

Natural Sciences and Engineering Research Council of Canada

Lockheed-Martin Cybersecurity Fund

Mitacs

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computational Theory and Mathematics,Hardware and Architecture,Signal Processing

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. MUSE: A Programmable Metadata Load Estimation Interface for Ceph File System;2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS);2023-12-17

2. An Adaptive Metadata Management Scheme Based on Deep Reinforcement Learning for Large-Scale Distributed File Systems;IEEE/ACM Transactions on Networking;2023-12

3. Low-Latency and Scalable Full-path Indexing Metadata Service for Distributed File Systems;2023 IEEE 41st International Conference on Computer Design (ICCD);2023-11-06

4. Optimization Strategies for Data Placement in Satellite Cloud-oriented Distributed File Systems;2023 IEEE International Conference on Joint Cloud Computing (JCC);2023-07

5. Efficient I/O Merging Scheme for Distributed File Systems;Symmetry;2023-02-05

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