VGRIS: virtualized GPU resource isolation and scheduling in cloud gaming

  • Authors:

  • Miao Yu;Chao Zhang;Zhengwei Qi;Jianguo Yao;Yin Wang;Haibing Guan

  • Affiliations:

  • Shanghai Key Laboratory of Scalable Computing and Systems. School of Software, Shanghai Jiao Tong University, Shanghai, China;Shanghai Key Laboratory of Scalable Computing and Systems. School of Software, Shanghai Jiao Tong University, Shanghai, China;Shanghai Key Laboratory of Scalable Computing and Systems. School of Software, Shanghai Jiao Tong University, Shanghai, China;Shanghai Key Laboratory of Scalable Computing and Systems. School of Software, Shanghai Jiao Tong University, Shanghai, China;HP Labs, Palo Alto, USA;Shanghai Key Laboratory of Scalable Computing and Systems. School of Software, Shanghai Jiao Tong University, Shanghai, China

  • Venue:
  • Proceedings of the 22nd international symposium on High-performance parallel and distributed computing
  • Year:
  • 2013

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Abstract

Fueled by the maturity of virtualization technology for Graphics Processing Unit (GPU), there is an increasing number of data centers dedicated to GPU-related computation tasks in cloud gaming. However, GPU resource sharing in these applications is usually poor. This stems from the fact that the typical cloud gaming service providers often allocate one GPU exclusively for one game. To achieve the efficiency of computational resource management, there is a demand for cloud computing to employ the multi-task scheduling technologies to improve the utilization of GPU. In this paper, we propose VGRIS, a resource management framework for Virtualized GPU Resource Isolation and Scheduling in cloud gaming. By leveraging the mature GPU paravirtualization architecture, VGRIS resides in the host through library API interception, while the guest OS and the GPU computing applications remain unmodified. In the proposed framework, we implemented three scheduling algorithms in VGRIS for different objectives, i.e., Service Level Agreement (SLA)-aware scheduling, proportional-share scheduling, and hybrid scheduling that mixes the former two. By designing such a scheduling framework, it is possible to handle different kinds of GPU computation tasks for different purposes in cloud gaming. Our experimental results show that each scheduling algorithm can achieve its goals under various workloads.