Current generative programming approaches use configuration knowledge to automatically manufacture an end product given a particular requirements specification. Such configuration knowledge models feature interactions either in the problem domain (at the requirements level) or in the solution domain (at the implementation level). Thus, feature interactions are defined as a composition problem in one specific phase of the generative programming lifecycle. However, we experienced the need to model and handle feature interactions that cross the problem and solution domain. This paper presents a specific case study, in the context of our work on distributed runtime adaptation, motivating this important but often ignored category of problem-solution feature interactions.