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ShareRender: Bypassing GPU Virtualization to Enable Fine-grained Resource Sharing for Cloud Gaming

Published: 19 October 2017 Publication History

Abstract

Cloud gaming is promising to provide high-quality game services by outsourcing game execution to cloud so that users can access games via thin clients (e.g., smartphones or tablets). However, existing cloud gaming systems su er from low GPU utilization in the virtualized environment. Moreover, GPU resources are scheduled in units of virtual machines (VMs) and this kind of coarse-grained scheduling at the VM-level fails to fully exploit GPU processing capacity. In this paper, we present ShareRender, a cloud gaming sys- tem that o oads graphics workloads within VMs directly to GPUs, bypassing GPU virtualization. For each game running in a VM, ShareRender starts a graphics wrapper to intercept frame rendering requests and assign them to render agents responsible for frame rendering on GPUs. Thanks to the exible workload assignment among multiple render agents, ShareRender enables ne-grained resource sharing at the frame-level to signi cantly improve GPU utilization. Further more, we design an online algorithm to determine workload assignment and migration of render agents, which considers the tradeo between minimizing the number of active server and low agent migration cost. We conduct experiments on real deployment and trace-driven simulations to evaluate the performance of ShareRender under di erent system settings. The results show that ShareRender outperforms the existing video-streaming-based cloud gaming system by over 4 times.

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    cover image ACM Conferences
    MM '17: Proceedings of the 25th ACM international conference on Multimedia
    October 2017
    2028 pages
    ISBN:9781450349062
    DOI:10.1145/3123266
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    Published: 19 October 2017

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    Author Tags

    1. cloud gaming
    2. gpu
    3. ne-grained
    4. scheduling

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    MM '17: ACM Multimedia Conference
    October 23 - 27, 2017
    California, Mountain View, USA

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    MM '17 Paper Acceptance Rate 189 of 684 submissions, 28%;
    Overall Acceptance Rate 2,145 of 8,556 submissions, 25%

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