用DECAF剖析云游戏性能

Hassan Iqbal, A. Khalid, Muhammad Shahzad
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引用次数: 0

摘要

云游戏平台在过去两年中取得了巨大的发展,包括亚马逊、Facebook、谷歌、微软和英伟达在内的许多大型互联网公司都公开推出了自己的平台。但是,目前还没有可以普遍应用的系统的业绩衡量方法。在本文中,我们实现了DECAF,这是一种系统地分析和剖析不同游戏类型和游戏平台的云游戏平台性能的方法。通过应用DECAF,我们测量了Google Stadia、Amazon Luna和Nvidia GeForceNow的性能,并发现了许多重要的发现,例如云中的处理延迟占总往返延迟的大部分(≈73.54%),这些平台提供的视频流具有比特率、帧率和分辨率的高可变性。我们的工作对云游戏平台具有重要意义,并为进一步研究云游戏的测量方法打开了大门。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Dissecting Cloud Gaming Performance with DECAF
Cloud gaming platforms have witnessed tremendous growth over the past two years, with a number of large Internet companies including Amazon, Facebook, Google, Microsoft, and Nvidia publicly launching their own platforms. However, there is an absence of systematic performance measurement methodologies which can generally be applied. In this paper, we implement DECAF, a methodology to systematically analyze and dissect the performance of cloud gaming platforms across different game genres and game platforms. By applying DECAF, we measure the performance of Google Stadia, Amazon Luna, and Nvidia GeForceNow, and uncover a number of important findings such as processing delays in the cloud comprise majority of the total round trip delay (≈73.54%), the video streams delivered by these platforms are characterized by high variability of bitrate, frame rate, and resolution. Our work has important implications for cloud gaming platforms and opens the door for further research on measurement methodologies for cloud gaming.
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