PNNL

Abstract

Data-intensive scientific workflows are often modeled using a dataflow-oriented model. The simplicity of a dataflow model facilitates intuitive workflow design, analysis, and optimization. However, some amount of control-flow modeling is often necessary for engineering fault-tolerant, robust, and adaptive workflows. Modeling the control-flow using inherent dataflow constructs will quickly end up with a workflow that is hard to comprehend, reuse, and maintain. In this paper, we propose a context-aware architecture for scientific workflows. By incorporating contexts within a data-flow oriented scientific workflow system, we enable the development of context-aware scientific workflows without the need to use numerous low-level control-flow actors. This results in a workflow that is aware of its environment during execution with minimal user input and responds intelligently based on such awareness at runtime. A further advantage of our approach is that the defined contexts can be reused and shared across other workflows. We demonstrate our approach with two prototype implementation of context-aware actors in KEPLER.