RTSBL: Reduce Task Scheduling Based on the Load Balancing and the Data Locality in Hadoop

  • Khadidja MidounEmail author
  • Walid-Khaled HidouciEmail author
  • Malik LoudiniEmail author
  • Djahida BelayadiEmail author
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 50)


We address load balancing and data locality problems in Hadoop. These two problems limit its performance, especially, during a reduce phase where the partitioning function assigns the keys to the reducers based on a hash function. We propose in this paper a new approach to assign the keys based on the reducers’ processing capability in order to ensure a good load balancing. In addition, our proposed approach called RTSBL takes into consideration the data locality during the partition. Our experiments prove that RTSBL achieves to up 87% improvements in the load balancing and 3\(\times \) improvements of the data locality during the reduce phase in the standard Hadoop.


MapReduce Hadoop Load balancing Data locality Reduce task scheduling 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Communication in Computer Systems LaboratoryNational High School of Computer ScienceOued-SmarAlgeria

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