Managing variability in workflow with feature model composition operators

Abstract

In grid-based scientific applications, building a workflow essentially involves composing parameterized services describing families of services and then configuring the resulting workflow product line. In domains (e.g., medical imaging) in which many different kinds of highly parameterized services exist, there is a strong need to manage variabilities so that scientists can more easily configure and compose services with consistency guarantees. In this paper, we propose an approach in which variable points in services are described with several separate feature models, so that families of workflow can be defined as compositions of feature models. A compositional technique then allows reasoning about the compatibility between connected services to ensure consistency of an entire workflow, while supporting automatic propagation of variability choices when configuring services. © 2010 Springer-Verlag.