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Improving the performance of Apache Hadoop on pervasive environments through context-aware scheduling


This article proposes to improve Apache Hadoop scheduling through a context-aware approach. Apache Hadoop is the most popular implementation of the MapReduce paradigm for distributed computing, but its design does not adapt automatically to computing nodes’ context and capabilities. By introducing context-awareness into Hadoop, we intent to dynamically adapt its scheduling to the execution environment. This is a necessary feature in the context of pervasive grids, which are heterogeneous, dynamic and shared environments. The solution has been incorporated into Hadoop and assessed through controlled experiments. The experiments demonstrate that context-awareness provides comparative performance gains, especially when some of the resources disappear during execution.

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Application programming interface


Distributed hash table


First in, first out


Hadoop distributed file system




Pervasive map-reduce project


Service-level agreement


Virtual machine


Yet another resource negotiator


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The authors would like to thank their partners in the PER-MARE project STIC-AmSud (2014) and acknowledge the financial support given to this research by the CAPES/MAEE/ANII STIC-AmSud collaboration program (project number 13STIC07).

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Correspondence to Luiz-Angelo Steffenel.

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W. Cassales, G., Schwertner Charão, A., Kirsch-Pinheiro, M. et al. Improving the performance of Apache Hadoop on pervasive environments through context-aware scheduling. J Ambient Intell Human Comput 7, 333–345 (2016).

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