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Network Anatomy and Real-Time Measurement of Nvidia GeForce NOW Cloud Gaming

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Passive and Active Measurement (PAM 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14537))

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Abstract

Cloud gaming, wherein game graphics is rendered in the cloud and streamed back to the user as real-time video, expands the gaming market to billions of users who do not have gaming consoles or high-power graphics PCs. Companies like Nvidia, Amazon, Sony and Microsoft are investing in building cloud gaming platforms to tap this large unserved market. However, cloud gaming requires the user to have high-bandwidth and stable network connectivity – whereas a typical console game needs about 100–200 kbps, a cloud game demands minimum 10–20 Mbps. This makes the Internet Service Provider (ISP) a key player in ensuring the end-user’s good gaming experience.

In this paper we develop a method to detect Nvidia’s GeForce NOW cloud gaming sessions over their network infrastructure, and measure associated user experience. In particular, we envision ISPs taking advantage of our method to provision network capacity at the right time and in the right place to support growth in cloud gaming at the right experience level; as well as identify the role of contextual factors such as user setup (browser vs app) and connectivity type (wired vs wireless) in performance degradation. We first present a detailed anatomy of flow establishment and volumetric profiles of cloud gaming sessions over multiple platforms, followed by a method to detect gameplay and measure key experience aspects such as latency, frame rate and resolution via real-time analysis of network traffic. The insights and methods are also validated in the lab for XBox Cloud Gaming platform. We then implement and deploy our method in a campus network to capture gameplay behaviors and experience measures across various user setups and connectivity types which we believe are valuable for network operators.

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Notes

  1. 1.

    We have obtained ethics clearance (UNSW Human Research Ethics Advisory Panel approval number HC211007) which allows us to analyze campus network traffic to infer application usage behaviors. Note that user identities remain anonymous – no attempt is made to extract or reveal any personal user information, and all results presented are aggregated across the campus.

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Acknowledgements

We thank our shepherd Alessandro Finamore and the four anonymous reviewers for their insightful feedback. Funding for this project is provided by the Australian Government’s Cooperative Research Centres Projects (CRC-P) Grant CRCPXIV000099.

Author information

Authors and Affiliations

  1. University of New South Wales, Sydney, Australia

    Minzhao Lyu & Vijay Sivaraman

  2. Canopus Networks Pty., Ltd., Sydney, Australia

    Sharat Chandra Madanapalli & Arun Vishwanath

Authors
  1. Minzhao Lyu
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  2. Sharat Chandra Madanapalli
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  3. Arun Vishwanath
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  4. Vijay Sivaraman
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Corresponding author

Correspondence to Minzhao Lyu .

Editor information

Editors and Affiliations

  1. Akamai Technologies, Cambridge, MA, USA

    Philipp Richter

  2. Hasso Plattner Institute, Potsdam, Germany

    Vaibhav Bajpai

  3. Northwestern University, Evanston, IL, USA

    Esteban Carisimo

Appendices

A Flow Profiles of GeForce NOW Cloud Game Sessions Across User Setups

As complimentary to the flow profiles for cloud game sessions via desktop console application given in Fig. 3, the ones via browser and mobile console applications are shown in Fig. 12. Comparing the three figures, they all have different usage of service flows for platform administration and game session management purposes. Sessions via both mobile and desktop console applications have five gameplay flows each with a unique functionality, whereas those via browsers only have two gameplay flows, one for management, and one for combined user input and game media streaming.

B Network Traffic Characteristics of XBox Cloud Gaming Sessions

We have conducted similar lab experiments on another cloud gaming platform Microsoft’s XBox Cloud Gaming that is currently available in Australia. We discuss flow profiles of gameplay sessions on XBox Cloud Gaming platform and the applicability of our developed methods.

Figure 13 shows the usage of service flows during cloud gaming sessions on XBox Cloud Gaming platform accessed via three supported user setups including XBox hardware console (Fig. 13(a)), PC browser (Fig. 13(b)), and mobile browser (Fig. 13(c)), respectively. Compared to the evolution of service flows in Nvidia GeForce NOW we discussed in Sect. 3, we observe similar insights. First of all, the purposes of flows are also categorized into platform administration, platform management, and gameplay; second, prior to each gameplay session, platform management flows (annotated as “regional-node”) are started to check current network connectivity and select appropriate cloud server; third, RTP flows that are destinated to a certain range of port numbers (e.g., UDP|1040 to UDP|1190) on the cloud server are used for gaming media and user input. Similar to GeForce NOW, the services being accessed in platform sessions of XBox Cloud Gaming vary across user setup. As shown by example service names in Fig. 13(a), 13(b) and 13(c), sessions accessed by XBox hardware console, PC browser and mobile browser uses different graphic services namely xgpuconsole, xgpuweb and xgpu, respectively.

Fig. 12.
figure 12

Flow profiles of example gameplay via different user setups. Service prefixes and port numbers of representative flows are shown by their respective y-ticks, and the throughput of each flow is shown by its thickness (normalized by logarithmic functions).

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Fig. 13.
figure 13

Flow profiles of XBox Cloud Gaming sessions via different user setups. Service prefixes and port numbers of representative flows are shown by their respective y-ticks, and the throughput of each flow is shown by its thickness (normalized by logarithmic functions).

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Apart from specific domain name (e.g., XBox Cloud Gaming uses xboxlive.com as its gameplay domain while GeForce NOW uses nvidiagrid.net) and range of service port numbers used on the cloud server that are different between XBox and GFN, we observed that XBox uses a single RTP flow for both streaming media and user input even on its native hardware console, while GFN uses separate RTP flows each only carry one type of traffic for sessions from console application.

It is not surprising to observe the above commonalities as cloud gaming platforms are built on similar technological paradigms and communication protocols. Therefore, our methods in detecting cloud gaming session, identifying user setup, and measuring gameplay QoE metrics are evidently applicable to XBox Cloud Gaming and other platforms with similar underlying structures.

C Charts Illustrating Classification of Gameplay Flows Using Heuristically Simplified Criteria

Figures 14 and 15 visually show the gameplay session flow classification processes for GeForce NOW and Xbox Cloud Gaming derived from our generic process shown in Fig. 8 with heuristically simplified criteria obtained from our training process on ground-truth traffic traces.

Fig. 14.
figure 14

Illustrative process for the classification of Nvidia’s GeForce NOW gameplay session flows, wherein the criteria obtained from our training process is annotated with . (Color figure online)

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Fig. 15.
figure 15

Illustrative process for the classification of Microsoft’s XBox Cloud Gaming gameplay session flows, wherein the criteria obtained from our training process is annotated with . (Color figure online)

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Lyu, M., Madanapalli, S.C., Vishwanath, A., Sivaraman, V. (2024). Network Anatomy and Real-Time Measurement of Nvidia GeForce NOW Cloud Gaming. In: Richter, P., Bajpai, V., Carisimo, E. (eds) Passive and Active Measurement. PAM 2024. Lecture Notes in Computer Science, vol 14537. Springer, Cham. https://doi.org/10.1007/978-3-031-56249-5_3

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  • DOI: https://doi.org/10.1007/978-3-031-56249-5_3

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