Abstract
Data intensive scientific workflows are often modelled using a dataflow-oriented model. The simplicity of a dataflow model facilitates intuitive workflow design, analysis and optimisation. However, some amount of control flow modelling is often necessary for engineering fault tolerant, robust and adaptive workflows. In scientific domain, myriads of environment information are needed for controlling different stages of execution. Modelling the control flow using inherent dataflow constructs will quickly result in 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 dataflow-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 approach results in a workflow that is aware of its environment during execution with minimal user input and that 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 implementations of context-aware actors in KEPLER.
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