MapReduce Applications in the Cloud: A Cost Evaluation of Computation and Storage

  • Diana Moise
  • Alexandra Carpen-Amarie
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7450)


MapReduce is a powerful paradigm that enables rapid implementation of a wide range of distributed data-intensive applications. The Hadoop project, its main open source implementation, has recently been widely adopted by the Cloud computing community. This paper aims to evaluate the cost of moving MapReduce applications to the Cloud, in order to find a proper trade-off between cost and performance for this class of applications. We provide a cost evaluation of running MapReduce applications in the Cloud, by looking into two aspects: the overhead implied by the execution of MapReduce jobs in the Cloud, compared to an execution on a Grid, and the actual costs of renting the corresponding Cloud resources. For our evaluation, we compared the runtime of 3 MapReduce applications executed with the Hadoop framework, in two environments: 1)on clusters belonging to the Grid’5000 experimental grid testbed and 2)in a Nimbus Cloud deployed on top of Grid’5000 nodes.


Virtual Machine Cloud Environment Cloud Resource Desktop Grid MapReduce Framework 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Diana Moise
    • 1
  • Alexandra Carpen-Amarie
    • 1
  1. 1.INRIA Rennes - Bretagne Atlantique / IRISAFrance

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