Engineering an autonomous fuzzy controller for QoS control in distributed services

Reinforcement Learning (RL) based approaches are now widely applied to building autonomous management applications with self-optimizing features. In this paper, we engineer an autonomous QoS controller based on a combination of fuzzy logic and RL to guarantee differentiated average response times for distributed services. We argue that there are two advantages of the fuzzy/RL QoS controller: the ability to autonomously tune if-then rules, and efficient modeling of the system state through fuzzy logic. Experiments conducted on a prototype show that the online learning fuzzy/RL controller can optimize its fuzzy rules effectively and efficiently and achieve better performance than manually tuned fuzzy controllers and the Self-Tuning Fuzzy Controller (STFC) used in.