MapIterativeReduce: A Framework for Reduction-Intensive Data Processing on Azure Clouds

Radu Tudoran 1 Alexandru Costan 1 Gabriel Antoniu 1
1 KerData - Scalable Storage for Clouds and Beyond
IRISA-D1 - SYSTÈMES LARGE ÉCHELLE, Inria Rennes – Bretagne Atlantique
Abstract : With the emergence of cloud computing as an alternative to supercomputers to support data intensive applications, MapReduce has arisen as a major programming model for data analysis on clouds. In this context, reduce-intensive algorithms are becoming increasingly useful in applications such as data clustering, classification and mining. However, platforms like MapReduce or Dryad lack built-in support for reduce-intensive workloads. This paper introduces MapIter- ativeReduce, a framework which 1) extends the MapReduce programming model to better support reduce-intensive ap- plications and 2) substantially improves their efficiency by eliminating the implicit barrier between the Map and the Reduce phase. We evaluated MapIterativeReduce on the Microsoft Azure cloud with synthetic benchmarks and with a real-life application. Compared to state-of-art solutions, our approach reduces the execution times by up to 75%
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https://hal.inria.fr/hal-00684814
Contributor : Gabriel Antoniu <>
Submitted on : Tuesday, April 3, 2012 - 11:33:42 AM
Last modification on : Friday, November 16, 2018 - 1:37:54 AM

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Radu Tudoran, Alexandru Costan, Gabriel Antoniu. MapIterativeReduce: A Framework for Reduction-Intensive Data Processing on Azure Clouds. Third International Workshop on MapReduce and its Applications (MAPREDUCE'12), held in conjunction with ACM HPDC'12., Jun 2012, Delft, Netherlands. pp.9-16, ⟨10.1145/2287016.2287019⟩. ⟨hal-00684814⟩

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