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

Authors:
Jia Rao;Xiangping Bu;Cheng-Zhong Xu;Leyi Wang;George Yin
Affiliations:
Wayne State University, Detroit, USA;Wayne State University, Detroit, USA;Wayne State University, Detroit, USA;Wayne State University, Detroit, USA;Wayne State University, Detroit, USA
Venue:
ICAC '09 Proceedings of the 6th international conference on Autonomic computing
Year:
2009

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Abstract

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 appropriate VM configurations. In this paper, we propose a reinforcement learning (RL) based approach, namely VCONF, to automate the VM configuration process. VCONF employs model-based RL algorithms to address the scalability and adaptability issues in applying RL in systems management. 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) configurations in small scale systems and shows good adaptability and scalability.