Research Output
A Survey of Speculative Execution Strategy in MapReduce
  MapReduce is a parallel computing programming model designed to process large-scale data. Therefore, the accuracy and efficiency for computing are needed to be assured and speculative execution is an efficient method for calculation of fault tolerance. It reaches the goals of shortening the execution time and increasing the cluster throughput through selecting slow tasks and speculative copy these tasks on a fast machine to be executed. Hadoop naïve speculative execution strategy assumes that the cluster is homogeneous, and this assumption leads to the poor performance in heterogeneous environment. Several speculative execution strategies which aim to improve the MapReduce Performance in the heterogeneous environments are reviewed in this paper like LATE, MCP, ex-MCP and ERUL, then the comparison between these methods are listed.

  • Date:

    01 November 2016

  • Publication Status:


  • Publisher

    Springer Nature

  • DOI:


  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    004 Data processing & computer science


Liu, Q., Jin, D., Liu, X., & Linge, N. (2016). A Survey of Speculative Execution Strategy in MapReduce. In A. Liu, H. C. Chao, E. Bertino, & X. Sun (Eds.), Cloud Computing and Security. , (296-307).



Hadoop, Map Reduce, Speculative execution, Heterogeneous environment

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