A grid workflow environment for brain imaging analysis on distributed systems

  • Authors:

  • Suraj Pandey;William Voorsluys;Mustafizur Rahman;Rajkumar Buyya;James E. Dobson;Kenneth Chiu

  • Affiliations:

  • Grid Computing and Distributed Systems (GRIDS) Laboratory, Department of Computer Science and Software Engineering, The University of Melbourne, Australia;Grid Computing and Distributed Systems (GRIDS) Laboratory, Department of Computer Science and Software Engineering, The University of Melbourne, Australia;Grid Computing and Distributed Systems (GRIDS) Laboratory, Department of Computer Science and Software Engineering, The University of Melbourne, Australia;Grid Computing and Distributed Systems (GRIDS) Laboratory, Department of Computer Science and Software Engineering, The University of Melbourne, Australia;Department of Psychological & Brain Sciences, Dartmouth College, U.S.A.;Grid Computing Research Lab, Department of Computer Science, State University of New York (SUNY) at Binghamton, NY, U.S.A.

  • Venue:
  • Concurrency and Computation: Practice & Experience - Special Issue: 3rd International Workshop on Workflow Management and Applications in Grid Environments (WaGe2008)
  • Year:
  • 2009

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Abstract

Scientific applications like neuroscience data analysis are usually compute and data-intensive. With the use of the additional capacity offered by distributed resources and suitable middlewares, we can achieve much shorter execution time, distribute compute and storage load, and add greater flexibility to the execution of these scientific applications than we could ever achieve in a single compute resource. In this paper, we present the processing of image registration (IR) for functional magnetic resonance imaging studies on Global Grids. We characterize the application, list its requirements and then transform it to a workflow. We use Gridbus Broker and Gridbus Workflow Engine technologies for executing the neuroscience application on the Grid. We developed a complete web-based portal integrating GUI-based workflow editor, execution management, monitoring and visualization of tasks and resources. We describe each component of the system in detail. We then execute the application on Grid'5000 platform and present extensive performance results. We show that the IR application can have (1) significantly improved makespan, (2) distribution of compute and storage load among resources used, and (3) flexibility when executing multiple times on Grid resources. Copyright © 2009 John Wiley & Sons, Ltd.