Uncertainty Reduction in Self-Adaptive Systems

Self-adaptive systems depend on models of themselves and their environment to decide whether and how to adapt, but these models are often affected by uncertainty. While current adaptation decision approaches are able to model and reason about this uncertainty, they do not consider ways to reduce it. This presents an opportunity for improving decision-making in self-adaptive systems, because reducing uncertainty results in a better characterization of the current and future states of the system and the environment (at some cost), which in turn supports making better adaptation decisions. We propose uncertainty reduction as the natural next step in uncertainty management in the field of self-adaptive systems. This requires both an approach to decide when to reduce uncertainty, and a catalog of tactics to reduce different kinds of uncertainty. We present an example of such a decision, examples of uncertainty reduction tactics, and describe how uncertainty reduction requires changes to the different activities in the typical self-adaptation loop.