A Cognitive Control System for Managing Runtime Uncertainty in Self-Integrating Autonomic Systems
Marius Pol, Ada Diaconescu · 2020
Autonomic systems manage resources to achieve high-level goals with minimum human intervention, by continuously adapting based on a model of themselves and of their environment. Adaptation requires managing runtime uncertainties produced by changes within a dynamic environment, by modeling unexpected situations that were not considered at design time. This challenge becomes even more critical in a self-integration context where autonomic systems, traditionally engineered systems, and human organizations, influence each other in a shared environment. In this paper, we propose a cognitive control system that aims to manage runtime uncertainty in self-integrating autonomic systems. The cognitive control system autonomously extends the existing modeling language, by generating knowledge to represent unexpected situations as they are encountered at runtime, and reasons on the resulting model, in order to determine the required adaptation knowledge. As proof-of-concept validation, we present a scenario supported by an implementation of our proposal, illustrating the capabilities of the cognitive control system in the smart home domain. The obtained results set the basis for further studying the modeling language for describing the adaptation knowledge, with the objective of enriching the collaboration capabilities of autonomic systems in a self-integration context as future work.