H2F: A Hierarchical Hadoop Framework for Big Data Processing in Geo-Distributed Environments

Big data analysis requires adequate infrastructure and programming paradigms capable of processing large amount of data. Hadoop, the most known open-source implementation of the MapReduce paradigm, is widely employed in big data analysis frameworks. However, in many recent application scenarios data are natively distributed over different geographic regions in data centers which are inter-connected through network links with very lower bandwidth than those of the computing environments where traditionally Hadoop deployments are supposed to work. In such a context, Hadoop applications perform very poorly. To cope with these issues, we developed a Hierarchical Hadoop Framework (H2F) specifically designed to work on geodistributed data. In this work, we compare the performance of H2F with that of a plain Hadoop implementation. First results show that for very large amount of data the H2F solution performs better than the Hadoop.