Context variability modeling for runtime configuration of service-based dynamic software product lines

Abstract

In emerging domains such as Cloud-based Industrial Control Systems (ICSs) and SCADA systems where data-intensive and high performance computing are needed, a higher degree of flexibility is being demanded to meet new stakeholder requirements, context changes and intrinsic complexity. In this light, Dynamic Software Product Lines (DSPLs) provide a way to build self-managing systems exploiting traditional product line engineering concepts at runtime. Although context-awareness is widely perceived to be a first-class concern in such runtime variability mechanisms, existing approaches do not provide the necessary level of formalization to model and enact context variability for DSPLs. This is crucial for operational analytics processes since variant configuration could differ from context to context depending on diverse data values linked to context features and cross-tree constraints in a feature model. In this paper, we propose a context variability modeling approach, demonstrate its applicability and usability via a wind farm use case, and present the fundamental building blocks of a framework for enabling context variability in service-based DSPLs which provide Workflow as a Service (WFaaS).