A Reinforcement Learning based Self-optimizing QoS Controller Framework for Distributed Services

摘要：

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.

关键词： QoS Control Self-optimizing Reinforcement Learning

作者: DaHai Li David Levy

作者单位: School of Electric and Information Engineering, University of Sydney, Sydney, N.S.W 2006

会议类型: 国际会议

会议名称: The 22nd China Control and Decision Conference(2010年中国控制与决策会议)

会议地点: 徐州

会议语种:英文

页码: 2917-2922

在线出版日期: 2010-05-26（万方平台首次上网日期，不代表论文的发表时间）