Performance optimization of a distributed transcoding system based on Hadoop for multimedia streaming services

Myoungjin Kim, Seungho Han, Yun Cui, Hanku Lee, Changsung Jeong

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


In recent times, significant progress has been achieved in cost-effective and timely processing of large amounts of data through Hadoop based on the emerging MapReduce framework. Based on these developments, we proposed a Hadoop-based Distributed Video Transcoding System which transcodes large video data sets into specific video formats depending on user-requested options. In order to reduce the transcoding time exponentially, we apply a Hadoop Distributed File System and a MapReduce framework to our system. Hadoop and MapReduce are designed to process petabyte-scale text data in a parallel and distributed manner. However, our system processes multi-media data. In this study, we measure the total transcoding time for various values of five MapReduce tuning parameters: block replication factor, Hadoop Distributed File System block size, Java Virtual Machine reuse option, maximum number of map slots and input/output buffer size. Thus, based on the experimental results, we determine the optimal values of the parameters affecting transcoding processing in order to improve the performance of our Hadoop-based system that processes a large amount of video data. From the results, it is clearly observed that our system exhibits a notable difference in transcoding performance depending on the values of the MapReduce tuning parameters.

Original languageEnglish
Pages (from-to)2099-2109
Number of pages11
JournalInformation (Japan)
Issue number5
Publication statusPublished - 2015 May 1


  • And cloud computing
  • Hadoop Optimization
  • MapReduce
  • Performance tuning
  • Video transcoding system

ASJC Scopus subject areas

  • Information Systems


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