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Efficient small file management in Hadoop distributed file system for enhanced e-government services

Author

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  • Fredrick Ishengoma

Abstract

Purpose - This paper introduces the Efficient Small File Management Algorithm (ESFMA) to overcome the challenge of small file inefficiency of Hadoop distributed file system (HDFS) for e-government services. Design/methodology/approach - ESFMA is designed with the following features: hierarchical metadata architecture, caching, block aggregation, prefetching and locality-aware data placement. These are intended to optimize NameNode memory usage, metadata handling, data block management, I/O and network performance. The algorithm was implemented in experiments on HDFS with real e-government small files. Findings - The experiments showed that ESFMA saves 10% of NameNode memory, 12% of metadata requests, 3.8% of data block use, 15% of read latency, 17% of write latency and 10% of network traffic. Practical implications - This study suggests that implementation of ESFMA has the potential to enable better e-government services in HDFS to be run efficiently and effectively. Originality/value - This paper presents an algorithm for small file management in HDFS, filling an important need in improving service efficiency and performance in e-government services.

Suggested Citation

  • Fredrick Ishengoma, 2025. "Efficient small file management in Hadoop distributed file system for enhanced e-government services," Technological Sustainability, Emerald Group Publishing Limited, vol. 4(2), pages 160-180, April.
    • Handle: RePEc:eme:techsp:techs-08-2024-0114
    DOI: 10.1108/TECHS-08-2024-0114
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