Grid approaches to data-driven scientific and engineering workflows

Abstract

Enabling the full life cycle of scientific and engineering workflows requires robust middleware and services that support near-realtime data movement, high-performance processing and effective data management. In this context, we consider two related technology areas: Grid computing which is fast emerging as an accepted way forward for the large-scale, distributed and multi-institutional resource sharing and Database systems whose capabilities are undergoing continuous change providing new possibilities for scientific data management in Grid.
In this thesis, we look into the challenging requirements while integrating data-driven scientific and engineering experiment workflows onto Grid. We consider wind tunnels that house multiple experiments with differing characteristics, as an application exemplar. This thesis contributes two approaches while attempting to tackle some of the following questions: How to allow domain-specific workflow activity development by hiding the underlying complexity? Can new experiments be added to the system easily? How can the overall turnaround time be reduced by an end-to-end experimental workflow support? In the first approach, we show how experiment-specific workflows can help accelerate application development using Grid services. This has been realized with the development of MyCoG, the first Commodity Grid toolkit for .NET supporting multi-language programmability. In the second , we present an alternative approach based on federated database services to realize an end-to-end experimental workflow. We show with the help of a real-world example, how database services can be building blocks for scientific and engineering workflows.

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