A write-friendly approach to manage namespace of Hadoop distributed file system by utilizing nonvolatile memory

Won Gi Choi, Sanghyun Park

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


With the emergence of the big data era, various technologies have been proposed to cope with the exascale of data. For a considerably large volume of data, a single machine does not comprise enough resources to store the complete data. Hadoop distributed file system (HDFS) enables large datasets to be stored across the big data environment consisting of several machines. Although Hadoop has become a crucial part of the big data industry, because of its simple architecture which composed of master and slaves several problems such as scalability and performance bottleneck has been remained to solve. New storage technologies offer an opportunity to solve the problems and improve HDFS. We propose a novel management scheme for namespace metadata of HDFS by utilizing nonvolatile memory which has been mentioned as the next-generation device since flash memory devices. Nonvolatile memory, which can guarantee data persistence and high performance with byte-address access, alleviates Namenode bottlenecks resulting from journaling processes performed to preserve the file system’s metadata. Our proposed methods show significant improvement compared with block devices such as hard disk drive, solid-state drive in terms of NameNode performance.

Original languageEnglish
Pages (from-to)6632-6662
Number of pages31
JournalJournal of Supercomputing
Issue number10
Publication statusPublished - 2019 Oct 1

Bibliographical note

Funding Information:
This research was supported by Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (NRF-2015M3C4A7065522).

Publisher Copyright:
© 2019, Springer Science+Business Media, LLC, part of Springer Nature.

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Information Systems
  • Hardware and Architecture


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