Parley+: Uncertainty Reduction in Self-Adaptive Systems

In its quest for approaches to taming uncertainty in self-adaptive systems (SAS), the research community has largely focused on solutions that adapt the SAS architecture or behaviour in response to uncertainty. By comparison, solutions that reduce the uncertainty affecting SAS (other than through the blanket monitoring of their components and environment) remain underexplored. Our previous work proposed Parley, a more nuanced, adaptive approach to SAS uncertainty reduction. To that end, we introduced a SAS architecture comprising an uncertainty reduction controller that drives the adaptive acquisition of new information within the SAS adaptation loop, and a tool-supported method that uses probabilistic model checking to synthesise such controllers. The controllers generated by our method deliver optimal trade-offs between SAS uncertainty reduction benefits and new information acquisition costs with guarantees for the satisfaction of requirements. In this paper, we extend Parley to Parley+ by improving the synthesis of these controllers, and by expanding the formalisation of Parley+ to prove the validity of the synthesis. We illustrate the use and extend the evaluation of the effectiveness of our approach for mobile robot navigation and service-based system SAS. The evaluation results show that Parley+ can synthesise controllers that help achieve the system’s objectives significantly better than Parley in 88.1% of the cases.