MapReduce across Distributed Clusters for Data-intensive Applications

• Published in: 2012 26th IEEE International Parallel and Distributed Processing Symposium Workshops pp. 2004 - 2011
• Main Authors: Lizhe Wang, Jie Tao, Marten, H., Streit, A., Khan, S. U., Kolodziej, J., Chen, D.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2012
• Subjects: Computational modeling; Computer architecture; Data Intensive Computing; Data processing; Distributed databases; Hadoop; MapReduce; Servers; Software; Torque
• ISBN: 1467309745; 9781467309745
• DOI: 10.1109/IPDPSW.2012.249

Recently, the computational requirements for large scale data-intensive analysis of scientific data have grown significantly. In High Energy Physics (HEP) for example, the Large Hadron Collider (LHC) produced 13 petabytes of data in 2010. This huge amount of data are processed on more than 140 computing centers distributed across 34 countries. The MapReduce paradigm has emerged as a highly successful programming model for large-scale data-intensive computing applications. However, current MapReduce implementations are developed to operate on single cluster environments and cannot be leveraged for large-scale distributed data processing across multiple clusters. On the other hand, workflow systems are used for distributed data processing across data centers. It has been reported that the workflow paradigm has some limitations for distributed data processing, such as reliability and efficiency. In this paper, we present the design and implementation of GHadoop, a MapReduce framework that aims to enable large-scale distributed computing across multiple clusters. G-Hadoop uses the Gfarm file system as an underlying file system and executes MapReduce tasks across distributed clusters. Experiments of the G-Hadoop framework on distributed clusters show encouraging results.