Design and development of an adaptive workflow-enabled spatial-temporal analytics framework

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.