Comparison of resource platform selection approaches for scientific workflows
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
Bridging workflow and data provenance using strong links
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
Elastic Cloud Caches for Accelerating Service-Oriented Computations
Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis
Future Generation Computer Systems
Data contracts for cloud-based data marketplaces
International Journal of Computational Science and Engineering
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Scientific workflows have gained popularity for modeling and executing in silico experiments by scientists for problem-solving. These workflows primarily engage in computation and data transformation tasks to perform scientific analysis in the Science Cloud. Increasingly workflows are gaining use in managing the scientific data when they arrive from external sensors and are prepared for becoming science ready and available for use in the Cloud. While not directly part of the scientific analysis, these workflows operating behind the Cloud on behalf of the ―data valets‖ play an important role in end-to-end management of scientific data products. They share several features with traditional scientific workflows: both are data intensive and use Cloud resources. However, they also differ in significant respects, for example, in the reliability required, scheduling constraints and the use of provenance collected. In this article, we investigate these two classes of workflows – Science Application workflows and Data Preparation workflows – and use these to drive common and distinct requirements from workflow systems for eScience in the Cloud. We use workflow examples from two collaborations, the NEPTUNE oceanography project and the Pan-STARRS astronomy project, to draw out our comparison. Our analysis of these workflows classes can guide the evolution of workflow systems to support emerging applications in the Cloud and the Trident Scientific Workbench is one such workflow system that has directly benefitted from this to meet the needs of these two eScience projects