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This paper describes an operator for configuring scientific workflows that facilitates the process of assigning workflow activities to cloud resources. In general, modeling and configuring scientific workflows is complex and error-prone, because workflows are built of highly parallel patterns comprising huge numbers of tasks. Reusing tested patterns as building blocks avoids repeating errors. Workflow...
Dataflow modeling is the natural way of composing scientific workflows, because they often comprise numerous data transformation steps applying massive parallelism. However, modeling control flow within dataflow is often achieved at the expense of clarity and comprehensibility. This paper describes scientific workflows maintaining the robustness of centralized control (using orchestration) by modeling...
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