Future Generation Computer Systems
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Scientific workflows have become increasingly popular as a new computing paradigm for scientists to design and execute complex and distributed scientific processes to enable and accelerate many scientific discoveries. Although several scientific workflow management systems (SWFMSs) have been developed, there is a great need for an integrated scientific workflow system that enables the design and execution of higher-level scientific workflows, which integrate heterogeneous scientific workflows enacted by existing SWFMSs. On one hand, science is becoming increasingly collaborative today, requiring an integrated solution that combines the features and capabilities of different SWFMSs, which are typically developed and optimized towards one single discipline. One the other hand, such an integrated environment can immediately leverage existing and emerging techniques and strengths of various SWFMSs and their supported execution environments, such as Cluster, Grid, and Cloud. The main contributions of this dissertation are: (1) We propose a scientific workflow system, called GENOMEFLOW, to design, develop, and execute higher-level scientific workflows, whose workflow tasks are themselves scientific workflows enacted by existing SWFMSs; (2) We propose a workflow scheduling algorithm, called GSA, to enable the parallel execution of such heterogeneous scientific workflows in their native heterogeneous environments; and (3) We implemented GENOMEFLOW towards the life science community and developed several GENOMEFLOW scientific workflows to demonstrate the capabilities of our system for genome data analysis applications.