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Parallel and Distributed Systems, International Conference on (2012)
Singapore, Singapore Singapore
Dec. 17, 2012 to Dec. 19, 2012
ISSN: 1521-9097
ISBN: 978-1-4673-4565-1
pp: 862-867
Cloud computing is a suitable platform for execution of complex computational tasks and scientific simulations that are described in the form of workflows. Such applications are managed by Workflow Management System (WfMS). Because existing WfMSs are not able to autonomically provision resources to real-time applications and schedule them while supporting fault tolerance and data privacy, we present a highly-scalable workflow-enabled analytics system that manages inter-dependable analytics tasks adaptively with varying operational requirements on a common platform and enables visualization of multidimensional datasets of real world phenomena. In this paper, we present the architecture of such a WfMS and evaluate it in terms of performance for execution of workflows in Clouds. A real world application of climate-associated dengue fever prediction was evaluated on public, private, and hybrid Clouds and experienced effective speedup in all the environments.
data privacy, workflow, cloud computing, dynamic resource, fault tolerance

X. Li et al., "Design and Development of an Adaptive Workflow-Enabled Spatial-Temporal Analytics Framework," 2012 IEEE 18th International Conference on Parallel and Distributed Systems (ICPADS), Singapore, 2012, pp. 862-867.
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