Towards adaptive monitoring for self-adaptive systems - Tapa blanda

Sinopsis

Nowadays, most of the approaches supporting self-adaptive systems rely on static feedback control loops for managing their adaptation process. One of the most popular feedback loops is the MAPE-K loop. In this loop, the Monitor element plays a crucial role since the quality of the monitoring data (e.g., timeliness, freshness, accuracy, availability, etc.) affects directly the performance of the rest of the elements of the loop, and in consequence the quality of the resulting adaptation decisions. Assuming static feedback loops implies that the structure and behavior of the elements of the loop should be determined at design time and cannot change at runtime, i.e., in the case of the Monitor, systems’ owners should know everything to be monitored at design time. If that is the case, current self-adaptive systems would not be able to react to unpredictable runtime events such as faults or changing requirements. Motivated by this fact, in this thesis we present HAFLoop (Highly Adaptive Feedback control Loop), an architectural proposal that extends the MAPE-K feedback loop for enabling and supporting the adaptation of the elements of the loop. We propose a generic structure for the adaptive elements as well as the mechanisms required for coordinating their operation with their adaptation process.In HAFLoop, the generic functionality of the elements of the loop is separated from the custom one, thus the proposed architecture can be reused by any approach for designing and developing self-adaptive systems with adaptive feedback loops. Given its importance, for validating HAFLoop, we focus on the adaptation of the Monitor element of the loop. Experiments are executed in the domain of smart vehicles and run in both simulation and real environments.

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