A Reinforcement Learning Approach to Online Web Systems Auto-Configuration
In a web system, configuration is crucial to the performance and service availability. It is a challenge, not only because of the dynamics of Internet traffic, but also the dynamic virtual machine environment the system tends to be run on. In this paper, the authors propose a reinforcement learning approach for autonomic configuration and reconfiguration of multi-tier web systems. It is able to adapt performance parameter settings not only to the change of workload, but also to the change of virtual machine configurations. The RL approach is enhanced with an efficient initialization policy to reduce the learning time for online decision. The approach is evaluated using TPC-W benchmark on a three-tier website hosted on a Xen-based virtual machine environment.