• DocumentCode
    3397096
  • Title

    Multicast-based replication for Hadoop HDFS

  • Author

    Jiadong Wu ; Bo Hong

  • fYear
    2015
  • fDate
    1-3 June 2015
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    The Hadoop HDFS is a popular open-source distributed storage system, which serves as the foundation of many important big-data technologies. The performance of data replication is crucial to HDFS, since it accounts for a major portion of network traffic in the entire cluster. In this research, we propose to enable multicast-based replication, which is expected to use less network bandwidth than the native TCP-based pipelined replication method. We developed a congestion-controlled reliable multicast socket (the CCRMSocket) for HDFS and evaluated its performance with our multi-rack test platform. The experimental result shows that our multicast implementation can effectively save bandwidth and peacefully coexist with TCP traffic. We also developed a simulator (the HFlowSim) to further study the impact of multicast-based replication to a large-scale Hadoop system. The simulation result suggests that multicast-based replication can systematically improve a Hadoop system by accelerating the big jobs.
  • Keywords
    Big Data; data handling; distributed databases; network operating systems; parallel processing; public domain software; Big-Data technology; CCRMSocket; Hadoop HDFS; congestion-controlled reliable multicast socket; data replication performance; large-scale Hadoop system; multicast-based replication; multirack test platform; native TCP-based pipelined replication method; network bandwidth; network traffic; open-source distributed storage system; Bandwidth; Computational modeling; Computers; Peer-to-peer computing; Sockets; Switches; Throughput;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD), 2015 16th IEEE/ACIS International Conference on
  • Conference_Location
    Takamatsu
  • Type

    conf

  • DOI
    10.1109/SNPD.2015.7176191
  • Filename
    7176191