Quality of Service Impact on Edge Physics Simulations for VR
Quality of Service Impact on Edge Physics Simulations for VR
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DOI：
10.1109/tvcg.2021.3067757
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发表时间：
2021-03
影响因子：
5.2
通讯作者：
Sebastian Friston;Elias J. Griffith;David Swapp;Caleb Lrondi;F. Jjunju;Ryan J. Ward;Alan Marshall;A. Steed
中科院分区：
文献类型：
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作者：
Sebastian Friston;Elias J. Griffith;David Swapp;Caleb Lrondi;F. Jjunju;Ryan J. Ward;Alan Marshall;A. Steed
移动头戴式显示器必须牺牲计算性能，以达到符合人体工程学和功率要求的扩展使用。因此，应用程序必须要么降低渲染和模拟的复杂性——以及丰富的体验——要么将复杂性转移到服务器上。在边缘计算的上下文中，一种流行的方法是通过渲染流。渲染流已经在桌面和主机上进行了演示。它也被用于hmd。然而，头部跟踪的延迟要求使这个应用程序更具挑战性。虽然移动gpu的性能还不如桌面gpu，但我们注意到它们正变得越来越强大和高效。由于VR的硬性要求，有必要继续研究哪些方案可以最佳地平衡负载、延迟和质量。我们提出了一种替代方案，我们称之为边缘物理：从运行在边缘资源上的模拟中在场景图级别流式传输，类似于集群渲染。场景流不仅简单，而且计算和带宽效率高。要求最高的循环在本地运行。那些碰到移动cpu电源墙的作业被卸载，而改进的gpu被利用，最大限度地提高计算利用率。在本文中，我们创建了一个原型实现，并评估了其在保真度，带宽和性能方面的潜力。我们表明，可以很容易地建立一个在典型的边链上保持高一致性的有效系统，但一些传统概念并不适用，并且需要更好地理解运动感知来全面评估这样的系统。
Mobile HMDs must sacrifice compute performance to achieve ergonomic and power requirements for extended use. Consequently, applications must either reduce rendering and simulation complexity - along with the richness of the experience - or offload complexity to a server. Within the context of edge-computing, a popular way to do this is through render streaming. Render streaming has been demonstrated for desktops and consoles. It has also been explored for HMDs. However, the latency requirements of head tracking make this application much more challenging. While mobile GPUs are not yet as capable as their desktop counterparts, we note that they are becoming more powerful and efficient. With the hard requirements of VR, it is worth continuing to investigate what schemes could optimally balance load, latency and quality. We propose an alternative we call edge-physics: streaming at the scene-graph level from a simulation running on edge-resources, analogous to cluster rendering. Scene streaming is not only straightforward, but compute and bandwidth efficient. The most demanding loops run locally. Jobs that hit the power-wall of mobile CPUs are off-loaded, while improving GPUs are leveraged, maximising compute utilisation. In this paper we create a prototypical implementation and evaluate its potential in terms of fidelity, bandwidth and performance. We show that an effective system which maintains high consistencies on typical edge-links can be easily built, but that some traditional concepts are not applicable, and a better understanding of the perception of motion is required to evaluate such a system comprehensively.