Abstract
Currently, the virtualization technologies for cloud computing infrastructures supporting extra devices, such as GPU, require additional development and refinement. This requirement is particularly evident in the area of resource sharing and allocation under some performance constraints, like the quality of service (QoS) guarantee, in light of the closed GPU platform. This deficiency significantly limits the applicability range of the cloud platform, which aims to support the efficient and fluent execution of business and academic workloads. This paper introduces gQoS, an adaptive virtualized GPU resource capacity sharing system under the QoS target, which can share and allocate the virtualized GPU resource among workloads adaptively, guaranteeing the QoS level with stability and accuracy. We evaluate the workloads and compare our gQoS strategy with other allocation strategies. The experiments show that our strategy guarantees much better accuracy and stability in QoS control and that the total GPU resource utilization under gQoS can be rewarded with at most a 25.85 percent reduction compared with other strategies.
Published Version
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