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

arXiv: https://arxiv.org/abs/2401.06366 · HTML: https://mlsanity.com/paper/2401.06366/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jan 2024.

## Built on by (most cited first, 3 of 3)

- [FastFlow: Early Yet Robust Network Flow Classification using the Minimal Number of Time-Series Packets](https://mlsanity.com/paper/2504.02174/) (2025)
- [Games Are Not Equal: Classifying Cloud Gaming Contexts for Effective User Experience Measurement](https://mlsanity.com/paper/2509.19669/) (2025)
- [From Prompts to Packets: A View from the Network on ChatGPT, Copilot, and Gemini](https://mlsanity.com/paper/2510.11269/) (2025)

## Abstract (arXiv)

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.