Automated Resource Sharing for Virtualized GPU with Self-Configuration
Jianguo Yao, Qiumin Lu, Zhengwei Qi · 2017
In this paper, we propose Auto-vGPU, a framework of automated resource sharing for virtualized GPU with self-configuration, to reduce manual intervention in system management while ensuring Service Level Agreement (SLA) targets. Auto-vGPU automatically collects the measurements of system metrics and learns a linear model for each application with dimension reduction. In order to fulfill the automated configuration of controller parameters, we propose a self-control-configuration method featuring the theory of automatic tuning of proportional-integral (PI) regulators. The experimental results of cloud gaming implementation demonstrate that Auto-vGPU is able to automatically build the low-dimension model and configure the control parameters without any manual interventions and the derived controller can adaptively allocate virtualized GPU resource to ensure the high performance of cloud applications.