Abstract
Existing work does not provide a flexible dataset-oriented data flow mechanism to meet the complex requirements of scientific Grid workflow applications. In this paper we present a sophisticated approach to this problem by introducing a data collection concept and the corresponding collection distribution constructs, which are inspired by HPF, however applied to Grid workflow applications. Based on these constructs, more fine-grained data flows can be specified at an abstract workflow language level, such as mapping a portion of a dataset to an activity, independently distributing multiple datasets, not necessarily with the same number of data elements, onto loop iterations. Our approach reduces data duplication, optimizes data transfers as well as simplifies the effort to port workflow applications onto the Grid. We have extended AGWL with these concepts and implemented the corresponding runtime support in ASKALON. We apply our approach to some real world scientific workflow applications and report performance results.
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