Vconf: a reinforcement learning approach to virtual machines auto-configuration

Virtual machine (VM) technology enables multiple VMs to share resources on the same host. Resources allocated to the VMs should be re-configured dynamically in response to the change of application demands or resource supply. Because VM execution involves privileged domain and VM monitor, this causes uncertainties in VMs ’ resource to performance mapping and poses challenges in online determination of ap-propriate VM configurations. In this paper, we propose a re-inforcement learning (RL) based approach, namely VCONF, to automate the VM configuration process. VCONF em-ploys model-based RL algorithms to address the scalability and adaptability issues in applying RL in systems manage-ment. Experimental results on both controlled environments and a testbed of clouds with Xen VMs and representative server workloads demonstrate the effectiveness of VCONF. The approach is able to find optimal (near optimal) config-urations in small scale systems and shows good adaptability and scalability