Self-adaptive smart spaces by proactive means-end reasoning

The ability of a system to change its behavior at run-time is one of the foundations for engineering intelligent environments. The vision of computing systems that can manage themselves is fascinating, but to date, it presents many intellectual challenges to face. Run-time goal-model artifacts represent a typical approach to communicate requirements to the system and open new directions for dealing with self-adaptation. This paper presents a theoretical framework and a general architecture for engineering self-adaptive smart spaces by breaking out some design-time constraints between goals and tasks. The architecture supports software evolution because goals may be changed during the application lifecycle. The architecture is responsible for configuring its components as the result of a decision-making algorithm working at the knowledge level. The approach is specifically suitable for developing smart space systems, promoting scalability and reusability. The proposed architecture is evaluated through the execution of a set of randomized stress tests.