H-WorD: Supporting Job Scheduling in Hadoop with Workload-Driven Data Redistribution

  • Petar Jovanovic
  • Oscar Romero
  • Toon Calders
  • Alberto Abelló
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9809)

Abstract

Today’s distributed data processing systems typically follow a query shipping approach and exploit data locality for reducing network traffic. In such systems the distribution of data over the cluster resources plays a significant role, and when skewed, it can harm the performance of executing applications. In this paper, we address the challenges of automatically adapting the distribution of data in a cluster to the workload imposed by the input applications. We propose a generic algorithm, named H-WorD, which, based on the estimated workload over resources, suggests alternative execution scenarios of tasks, and hence identifies required transfers of input data a priori, for timely bringing data close to the execution. We exemplify our algorithm in the context of MapReduce jobs in a Hadoop ecosystem. Finally, we evaluate our approach and demonstrate the performance gains of automatic data redistribution.

Keywords

Data-intensive flows Task scheduling Data locality 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Petar Jovanovic
    • 1
  • Oscar Romero
    • 1
  • Toon Calders
    • 2
    • 3
  • Alberto Abelló
    • 1
  1. 1.Universitat Politècnica de Catalunya, BarcelonaTechBarcelonaSpain
  2. 2.Universite Libre de BruxellesBrusselsBelgium
  3. 3.University of AntwerpAntwerpBelgium

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