论文标题

云获奖:Google Stadia流量的分析

Cloud-gaming:Analysis of Google Stadia traffic

论文作者

Carrascosa, Marc, Bellalta, Boris

论文摘要

互动,实时和高质量的云视频游戏对互联网构成了严重的挑战,这是由于同时高通量和往返延迟较低的要求。在本文中,我们调查了Google的云游戏解决方案Stadia的交通特征,该解决方案很可能成为游戏领域的主要玩家之一。为此,我们设计了几个实验,并执行广泛的交通测量活动以获取所有必需的数据。我们的第一个目标是通过确定信号和视频/音频内容,交通生成模式以及数据包大小和包装间时间概率分布的不同协议来深入了解体育馆的流量特征。然后,我们的第二个目标是了解不同的Stadia游戏和配置(例如视频编解码器和选择的视频分辨率)如何影响生成的流量的特征。我们还评估了体育馆适应不同链路容量条件的能力,包括突然容量下降的情况以及网络潜伏期的突然增加。除了说明体育馆流量的特征外,我们的结果和发现对于规划和尺寸尺寸以及设计新的资源管理策略也很有价值。最后,我们将Stadia流量与其他视频流应用程序进行了比较,展示了它们之间的主要区别,并使用我们的捕获介绍了交通模型。我们表明,该模型可以用于模拟中,以进一步研究Stadia流量存在的网络性能。

Interactive, real-time, and high-quality cloud video games pose a serious challenge to the Internet due to simultaneous high-throughput and low round trip delay requirements. In this paper, we investigate the traffic characteristics of Stadia, the cloud-gaming solution from Google, which is likely to become one of the dominant players in the gaming sector. To do that, we design several experiments, and perform an extensive traffic measurement campaign to obtain all required data. Our first goal is to gather a deep understanding of Stadia traffic characteristics by identifying the different protocols involved for both signalling and video/audio contents, the traffic generation patterns, and the packet size and inter-packet time probability distributions. Then, our second goal is to understand how different Stadia games and configurations, such as the video codec and the video resolution selected, impact on the characteristics of the generated traffic. We also evaluate the ability of Stadia to adapt to different link capacity conditions, including cases where the capacity drops suddenly, as well as sudden increases in the network latency. Our results and findings, besides illustrating the characteristics of Stadia traffic, are also valuable for planning and dimensioning future networks, as well as for designing new resource management strategies. Finally, we compare Stadia traffic to other video streaming applications, showcasing the main differences between them, and introduce a traffic model using our captures. We show that this model can be used in simulations to further investigate the network performance in presence of Stadia traffic.

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