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.

In cloud gaming systems, the game program runs at servers in the cloud, while clients access game services by sending input events to the servers and receiving game scenes via video streaming. In this paradigm, servers are responsible for all performance-intensive operations, and thus suffer from poor scalability. An alternative paradigm is called graphics streaming, in which graphics commands and data are offloaded to the clients for local rendering, thereby mitigating the server's burden and allowing more concurrent game sessions. Unfortunately, this approach is bandwidth-consuming, due to large amounts of graphic commands and geometry data. In this paper, we present LiveRender, an open-source gaming system that remedies the problem by implementing a suite of bandwidth optimization techniques including intraframe compression, interframe compression, and caching, establishing what we call compressed graphics streaming. Experiments results show that the new approach is able to reduce bandwidth consumption by 52%-73% compared to raw graphics streaming, with no perceptible difference in video quality and reduced response delay. Compared to the video streaming approach, LiveRender achieves a traffic reduction of 40%-90% with even improved video quality and substantially smaller response delay, while enabling higher concurrency at the server.

In cloud gaming systems, the game program runs at servers in the cloud, while clients access game services by sending input events to the servers and receiving game scenes via video streaming. In this paradigm, servers are responsible for all performance-intensive operations, and thus suffer from poor scalability. An alternative paradigm is called graphics streaming, in which graphics commands and data are offloaded to the clients for local rendering, thereby mitigating the server's burden and allowing more concurrent game sessions. Unfortunately, this approach is bandwidth consuming, due to large amounts of graphic commands and geometry data. In this paper, we present LiveRender, an open source gaming system that remedies the problem by implementing a suite of bandwidth optimization techniques including intra-frame compression, inter-frame compression, and caching, establishing what we call compressed graphics streaming. Experiments results show that the new approach is able to reduce bandwidth consumption by 52-73% compared to raw graphics streaming, with no perceptible difference in video quality and reduced response delay. Compared with the video streaming approach, LiveRender achieves a traffic reduction of 40-90% with even improved video quality and substantially smaller response delay, while enabling higher concurrency at the server.