Kepler + Hadoop: a general architecture facilitating data-intensive applications in scientific workflow systems

Abstract

MapReduce provides a parallel and scalable programming model for data-intensive business and scientific applications. MapReduce and its de facto open source project, called Hadoop, support parallel processing on large datasets with capabilities including automatic data partitioning and distribution, load balancing, and fault tolerance management. Meanwhile, scientific workflow management systems, e.g., Kepler, Taverna, Triana, and Pegasus, have demonstrated their ability to help domain scientists solve scientific problems by synthesizing different data and computing resources. By integrating Hadoop with Kepler, we provide an easy-to-use architecture that facilitates users to compose and execute MapReduce applications in Kepler scientific workflows. Our implementation demonstrates that many characteristics of scientific workflow management systems, e.g., graphical user interface and component reuse and sharing, are very complementary to those of MapReduce. Using the presented Hadoop components in Kepler, scientists can easily utilize MapReduce in their domain-specific problems and connect them with other tasks in a workflow through the Kepler graphical user interface. We validate the feasibility of our approach via a word count use case.