• DocumentCode
    249392
  • Title

    MapCheckReduce: An Improved MapReduce Computing Model for Imprecise Applications

  • Author

    Changjian Wang ; Yuxing Peng ; Mingxing Tang ; Dongsheng Li ; Shanshan Li ; Pengfei You

  • Author_Institution
    Coll. of Comput., Nat. Univ. of Defense Technol., Changsha, China
  • fYear
    2014
  • fDate
    June 27 2014-July 2 2014
  • Firstpage
    366
  • Lastpage
    373
  • Abstract
    Optimizing the Map Phase is an important way to reduce the MapReduce (MR) job runtime. The common way for such studies is to design more efficient scheduling policies. However, there exists a kind of MapReduce applications, named imprecise applications, where the reduce phase can be completed based on the outputs of part of the map tasks. According to the feature of imprecise applications, we propose an improved MapReduce model, named MapCheckReduce (MCR). MCR can terminate the map process when the requirements of imprecise applications are satisfied. Compared to MR, a Check mechanism and a set of extended programming interfaces are added to MCR. The Check mechanism can receive and analyze messages submitted by mappers and then determine whether to terminate the map phase. The programming interfaces can be used by the programmers of imprecise applications to define the termination conditions of the map phase. The MCR prototype has been implemented and experiment results verify the feasibility and effectiveness of MCR.
  • Keywords
    cloud computing; parallel programming; scheduling; MCR; MapCheckReduce; MapReduce computing model; MapReduce job runtime reduction; cloud computing; map phase optimization; phase reduction; programming interfaces; scheduling policies; Big data; Computational modeling; Data models; Educational institutions; Programming; Runtime; Imprecise Applications; MapCheckReduce; MapReduce;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Big Data (BigData Congress), 2014 IEEE International Congress on
  • Conference_Location
    Anchorage, AK
  • Print_ISBN
    978-1-4799-5056-0
  • Type

    conf

  • DOI
    10.1109/BigData.Congress.2014.61
  • Filename
    6906804