Reflexive and Evolutional Digital Service Ecosystems with Models at Runtime

Uncertainty in digital service ecosystems (DSEs) can be attributed to several factors like the dynamic nature of the ecosystem and unknown deployment environment, change and evolution of requirements, and co-evolution among ecosystem members. Managing uncertainties in DSEs is challenging, and therefore, novel and solid software architecting methods, techniques and tools are needed. Our research explores the means to handle uncertainties at the software architecture level of DSEs. In this regard, we apply valuable lessons learnt from the models at runtime (M@RT) technique. This paper proposes a novel, dynamic knowledge engineering approach to handle uncertainties in DSEs at runtime using M@RT. This uncertainty handling approach aims to identify and solve two interrelated research problems: reflexivity and evolution of the ecosystem between the architecture and running system of services. Reflexivity means that the system must have knowledge of its components to make intelligent decisions based on selfawareness. In addition, we provide tool support towards automating reflexivity and evolution. Complex state machines of M@RT that serve as a dynamic knowledgebase are modeled using executable state machines, and generation of software artifacts of the model is performed at execution time. Causal connection is maintained between the runtime models and the running system. We validate and illustrate our approach using a DSE in an ambient-assisted living environment for elderly