A reinforcement learning based self-optimizing QoS controller framework for distributed services

QoS control is complicated by limited knowledge of system characteristics and continuous evolving features in modern distributed systems. In this paper, we propose a practical reinforcement learning based self-optimized QoS controller algorithm with the ability to guarantee differentiated average response time requirements for different service classes. The proposed solution consists of two key contributions: the reinforcement learning based self-optimizing control algorithm and full evaluations of this control algorithm. Experiments on the prototype show that standard reinforcement learning can learn and self- optimize the control knowledge efficiently in feasible training time with only partial system knowledge.