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VGRIS: virtualized GPU resource isolation and scheduling in cloud gaming

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Published:17 June 2013Publication History

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.

References

  1. P. Barham, B. Dragovic, K. Fraser, S. Hand, T. L. Harris, A. Ho, R. Neugebauer, I. Pratt, and A. Warfield. Xen and the art of virtualization. In Proceedings of ACM Symposium on Operating Systems Principles, SOSP, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. M. Bautin, A. Dwarakinath, and T. cker Chiueh. Graphic engine resource management. In Proceedings of Multimedia Computing and Networking, MMCN, 2008.Google ScholarGoogle Scholar
  3. M. Becchi, K. Sajjapongse, I. Graves, A. Procter, V. Ravi, and S. Chakradhar. A virtual memory based runtime to support multi-tenancy in clusters with GPUs. In Proceedings of international symposium on High-Performance Parallel and Distributed Computing, HPDC, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. L. Chen, O. Villa, S. Krishnamoorthy, and G. R. Gao. Dynamic load balancing on single- and multi-gpu systems. In Proceedinigs of IEEE International Symposium on Parallel Distributed Processing, IPDPS, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  5. L. Cheng, A. Bhushan, R. Pajarola, and M. E. Zarki. Realtime 3D graphics streaming using MPEG-4. In Proceedings of the nineteenth ACM symposium on Operating systems principles, BroadWise, 2004.Google ScholarGoogle Scholar
  6. L. Cherkasova, D. Gupta, and A. Vahdat. Comparison of the three CPU schedulers in Xen. SIGMETRICS Performance Evaluation Review, 35(2):42--51, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. M. Dowty and J. Sugerman. GPU virtualization on VMware's hosted I/O architecture. SIGOPS Operating Systems Review, 43:73--82, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. J. Duato, F. D. Igual, R. Mayo, A. J. Pena, E. S. Quintana-Orti, and F. Silla. An efficient implementation of GPU virtualization in high performance clusters. In Proceedings of European Conference on Parallel Processing, Euro-Par Workshops, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. J. Duato, A. J. Péna, F. Silla, R. Mayo, and E. S. Quintana-Ortí. rCUDA: Reducing the number of GPU-based accelerators in high performance clusters. In Proceedings of the International Conference on High Performance Computing and Simulation, HPCS, 2010.Google ScholarGoogle ScholarCross RefCross Ref
  10. K. J. Duda and D. R. Cheriton. Borrowed-virtual-time (BVT) scheduling: supporting latency-sensitive threads in a general-purpose scheduler. In Proceedings of the ACM Symposium on Operating Systems Principles, SOSP, 1999. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. A. Dwarakinath. A fair-share scheduler for the graphics processing unit. Master Thesis, 2008.Google ScholarGoogle Scholar
  12. G. A. Elliott and J. H. Anderson. Globally scheduled real-time multiprocessor systems with GPUs. Real-Time Systems, 48(1):34--74, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. V. Gupta, A. Gavrilovska, K. Schwan, H. Kharche, N. Tolia, V. Talwar, and P. Ranganathan. GViM: Gpu-accelerated virtual machines. In Proceedings of the ACM Workshop on System-level Virtualization for High Performance Computing, HPCVirt, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. V. Gupta, K. Schwan, N. Tolia, V. Talwar, and P. Ranganathan. Pegasus: Coordinated scheduling for virtualized accelerator-based systems. In Proceedings of the 2011 USENIX conference on USENIX annual technical conference, ATC, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Joystiq. GDC09 interview: OnLive founder Steve Perlman, continued. http://www.joystiq.com/2009/04/02/gdc09-interview-onlive-founder-steve-perlman-continued/.Google ScholarGoogle Scholar
  16. A. Jurgelionis, P. Fechteler, P. Eisert, F. Bellotti, H. David, J.-P. Laulajainen, R. Carmichael, V. Poulopoulos, A. Laikari, P. H. J. Perala, A. D. Gloria, and C. Bouras. Platform for distributed 3D gaming. Int. J. Computer Games Technology, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. S. Kato, K. Lakshmanan, Y. Ishikawa, and R. R. Rajkumar. Resource sharing in GPU-accelerated windowing systems. In Proceedings of the 2011 17th IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. S. Kato, K. Lakshmanan, R. Rajkumar, and Y. Ishikawa. TimeGraph: GPU scheduling for real-time multi-tasking environments. In Proceedings of the 2011 USENIX conference on USENIX annual technical conference, ATC, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. M. Kesavan, A. Gavrilovska, and K. Schwan. Differential virtual time (DVT): rethinking I/O service differentiation for virtual machines. In Proceedings of the 1st ACM symposium on Cloud computing, SoCC, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. H. A. Lagar-Cavilla, N. Tolia, M. Satyanarayanan, and E. de Lara. VMM-independent graphics acceleration. In Proceedings of the International Conference on Virtual Execution Environments, VEE, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. M. Lee, A. S. Krishnakumar, P. Krishnan, N. Singh, and S. Yajnik. Supporting soft real-time tasks in the Xen hypervisor. In Proceedings of the 6th ACM SIGPLAN/SIGOPS international conference on Virtual execution environments, VEE, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. L. Li, X. Li, G. Tan, M. Chen, and P. Zhang. Experience of parallelizing cryo-em 3D reconstruction on a CPU-GPU heterogeneous system. In Proceedings of the ACM International Symposium on High Performance Distributed Computing, HPDC, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. K. Maeda, M. Murase, M. Doi, H. Komatsu, S. Noda, and R. Himeno. Automatic resource scheduling with latency hiding for parallel stencil applications on GPGPU clusters. In Proceedinigs of IEEE International Symposium on Parallel Distributed Processing, IPDPS, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. A. M. Merritt, V. Gupta, A. Verma, A. Gavrilovska, and K. Schwan. Shadowfax: scaling in heterogeneous cluster systems via GPGPU assemblies. In Proceedings of the 5th international workshop on Virtualization technologies in distributed computing, VTDC, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. B. H. Ng, B. Lau, and A. Parkash. Direct access to graphics card leveraging VT-d. Technical report, University of Michigan, 2009.Google ScholarGoogle Scholar
  26. Y. Noimark and D. Cohen-Or. Streaming scenes to MPEG-4 video-enabled devices. IEEE Computer Graphics and Applications, 23(1):58--64, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. J. Owens, M. Houston, D. Luebke, S. Green, J. Stone, and J. Phillips. GPU computing. Proceedings of the IEEE, 96(5):879--899, 2008.Google ScholarGoogle ScholarCross RefCross Ref
  28. R. Phull, C.-H. Li, K. Rao, S. Cadambi, and S. T. Chakradhar. Interference-driven resource management for GPU-based heterogeneous clusters. In Proceedings of the ACM International Symposium on High Performance Distributed Computing, HPDC, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. V. T. Ravi, M. Becchi, G. Agrawal, and S. T. Chakradhar. Supporting GPU sharing in cloud environments with a transparent runtime consolidation framework. In Proceedings of the ACM International Symposium on High Performance Distributed Computing, HPDC, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. L. Shi, H. Chen, and J. Sun. vCUDA: GPU accelerated high performance computing in virtual machines. In Proceedinigs of IEEE International Symposium on Parallel Distributed Processing, IPDPS, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. M. Stillwell, F. Vivien, and H. Casanova. Virtual machine resource allocation for service hosting on heterogeneous distributed platforms. In Proceedinigs of IEEE International Symposium on Parallel Distributed Processing, IPDPS, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Y. Wang and A. Merchant. Proportional-share scheduling for distributed storage systems. In Proccedings of the 5th conference on File and storage technologies, FAST, 2007. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. D. D. Winter, P. Simoens, L. Deboosere, F. D. Turck, J. Moreau, B. Dhoedt, and P. Demeester. A hybrid thin-client protocol for multimedia streaming and interactive gaming applications. In Proceedings of the International Workshop on Network and Operating Systems Support for Digital Audio and Video, NOSSDAV, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. C. Xu, S. Gamage, P. N. Rao, A. Kangarlou, R. R. Kompella, and D. Xu. vSlicer: latency-aware virtual machine scheduling via differentiated-frequency CPU slicing. In Proceedings of the ACM International Symposium on High Performance Distributed Computing, HPDC, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. P. Yu, M. Xia, Q. Lin, M. Zhu, S. Gao, Z. Qi, K. Chen, and H. Guan. Real-time enhancement for Xen hypervisor. In Proceedings of Embedded and Ubiquitous Computing, EUC, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. J. Zhang, A. Sivasubramaniam, Q. Wang, A. Riska, and E. Riedel. Storage performance virtualization via throughput and latency control. Trans. Storage, 2:283--308, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library

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        • Published in

          cover image ACM Conferences
          HPDC '13: Proceedings of the 22nd international symposium on High-performance parallel and distributed computing
          June 2013
          276 pages
          ISBN:9781450319102
          DOI:10.1145/2493123
          • General Chairs:
          • Manish Parashar,
          • Jon Weissman,
          • Program Chairs:
          • Dick Epema,
          • Renato Figueiredo

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          Publication History

          • Published: 17 June 2013

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          HPDC '13 Paper Acceptance Rate20of131submissions,15%Overall Acceptance Rate166of966submissions,17%

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