2012 International Conference on Advanced Computer Science Applications and Technologies (ACSAT) (2012)
Nov. 26, 2012 to Nov. 28, 2012
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ACSAT.2012.70
Cloud computing has introduced an utility computing model which offers an alternative to traditional servers and computing clusters. Due to fault tolerance and scalability feature, Map Reduce distributed data processing architecture has now become the choice for data-intensive analysis in the clouds. Recently Mapruduce is used as a service or for setting up one's own Map reduce cluster. In this paper we evaluate the architecture and performance of different Map Reduce framework, such as AzureMapreduce built using the Microsoft Azure cloud infrastructure, Cloud Map Reduce(CMR) built on top of Amazon cloud OS. We belief that the techniques we discussed in this paper are general enough that would encouraged others to use them in other applications. Our survey would definitely open an promising approach to improve Map Reduce performance for Cloud Computing.
cloud computing, software fault tolerance, software performance evaluation
N. Nurain, H. Sarwar, M. P. Sajjad and M. Mostakim, "An In-depth Study of Map Reduce in Cloud Environment," 2012 International Conference on Advanced Computer Science Applications and Technologies (ACSAT), Kuala Lumpur, 2013, pp. 263-268.