Mapreduce Challenges on Pervasive Grids

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 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.

Efficient Prototyping of Fault Tolerant Map-Reduce Applications with Docker-Hadoop

“…We believe that this twofold approach is essential to understand and cover all the problem issues. In the context of the PER-MARE project, this paper focus on improving Hadoop fault-tolerance and context-awareness [23], enabling its deployment over a pervasive environment [24]. Figure 5.…”

Cuckoo: Opportunistic MapReduce on Ephemeral and Heterogeneous Cloud Resources

“…There are some studies that handled volatility in open systems by using a small set of dedicated nodes in order to ensure the minimum amount of resources required to execute MapReduce jobs [16]. In [17] authors considered the case of pervasive grids and monitored nodes capacity (i.e., processors and memory) to improve Hadoop scheduler decisions. Differently from those previous work, our contribution targets environments such as private Cloud data centers.…”