چکیده (انگلیسی):

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 propose
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.