Article citationsMore>>

Ekanayake, J., Pallickara, S. and Fox, G. (2008) MapReduce for Data Intensive Scientific Analyses. IEEE Fourth International Conference on eScience, Indianapolis, 7-12 December 2008, 277-284.

has been cited by the following article:

• TITLE: Assessment, Design and Implementation of a Private Cloud for MapReduce Applications

  AUTHORS: M. Salgueiro, P. González, T. F. Pena, J. C. Cabaleiro

  KEYWORDS: MapReduce, Hadoop as a Service, OpenStack, Private Clouds, Cloud Computing, Virtualization

  JOURNAL NAME: Open Access Library Journal, Vol.1 No.3, June 18, 2014

  ABSTRACT: Scientific computation and data intensive analyses are ever more frequent. On the one hand, the MapReduce programming model has gained a lot of attention for its applicability in large parallel data analyses and Big Data applications. On the other hand, Cloud computing seems to be increasingly attractive in solving these computing problems that demand a lot of resources. This paper explores the potential symbiosis between MapReduce and Cloud Computing, in order to create a robust and scalable environment to execute MapReduce workflows regardless of the underlaying infrastructure. The main goal of this work is to provide an easy-to-install interface, so as non-expert scientists can deploy a suitable testbed for their MapReduce experiments on local resources of their institution. Testing cases were performed in order to evaluate the required time for the whole executing process on a real cluster.