Journal of Computer Science

MAPREDUCE CHALLENGES ON PERVASIVE GRIDS

L. A. Steffenel, O. Flauzac, A. S. Charao, P. P. Barcelos, B. Stein, G. Cassales, S. Nesmachnow, J. Rey, M. Cogorno, M. Kirsch-Pinheiro and C. Souveyet

DOI : 10.3844/jcssp.2014.2194.2210

Journal of Computer Science

Volume 10, Issue 11

Pages 2194-2210

Abstract

This study presents the advances on designing and implementing scalable techniques to support the development and execution of MapReduce application in pervasive distributed computing infrastructures, in the context of the PER-MARE project. A pervasive framework for MapReduce applications is very useful in practice, especially in those scientific, enterprises and educational centers which have many unused or underused computing resources, which can be fully exploited to solve relevant problems that demand large computing power, such as scientific computing applications, big data processing, etc. In this study, we pro-pose the study of multiple techniques to support volatility and heterogeneity on MapReduce, by applying two complementary approaches: Improving the Apache Hadoop middleware by including context-awareness and fault-tolerance features; and providing an alternative pervasive grid implementation, fully adapted to dynamic environments. The main design and implementation decisions for both alternatives are described and validated through experiments, demonstrating that our approaches provide high reliability when executing on pervasive environments. The analysis of the experiments also leads to several insights on the requirements and constraints from dynamic and volatile systems, reinforcing the importance of context-aware information and advanced fault-tolerance features to provide efficient and reliable MapReduce services on pervasive grids.

Copyright

© 2014 L. A. Steffenel, O. Flauzac, A. S. Charao, P. P. Barcelos, B. Stein, G. Cassales, S. Nesmachnow, J. Rey, M. Cogorno, M. Kirsch-Pinheiro and C. Souveyet. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.