research-article

MetaVRadar: Measuring Metaverse Virtual Reality Network Activity

Authors:

Rahul Dev Tripathi,

Vijay SivaramanAuthors Info & Claims

Proceedings of the ACM on Measurement and Analysis of Computing Systems, Volume 7, Issue 3

Article No.: 55, Pages 1 - 29

https://doi.org/10.1145/3626786

Published: 12 December 2023 Publication History

Abstract

The "metaverse", wherein users can enter virtual worlds to work, study, play, shop, socialize, and entertain, is fast becoming a reality, attracting billions of dollars in investment from companies such as Meta, Microsoft, and Clipo Labs. Further, virtual reality (VR) headsets from entities like Oculus, HTC, and Microsoft are rapidly maturing to provide fully immersive experiences to metaverse users. However, little is known about the network dynamics of metaverse VR applications in terms of service domains, flow counts, traffic rates and volumes, content location and latency, etc., which are needed to make telecommunications network infrastructure "metaverse ready" to support superlative user experience in the coming future.

This paper is an empirical measurement study of metaverse VR network behavior aimed at helping telecommunications network operators better provision and manage the network to ensure good user experience. Using illustrative hour-long network traces of metaverse sessions on the Oculus VR headset, we first develop a categorization of user activity into distinct states ranging from login home to streetwalking and event attendance to asset trading, and undertake a detailed analysis of network traffic per state, identifying unique service domains, protocols, flow profiles, and volumetric patterns, thereby highlighting the vastly more complex nature of a metaverse session compared to streaming video or gaming. Armed with the network behavioral profiles, our second contribution develops a real-time methodMetaVRadar to detect metaverse session and classify the user activity state leveraging formalized flow signatures and volumetric attributes. Our third contribution practically implementsMetaVRadar, evaluates its accuracy in our lab environment, and demonstrates its usability in a large university network so operators can better monitor and plan resources to support requisite metaverse user experience.

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Cited By

Lyu MWang YSivaraman V(2024)Measuring GenAI Usage Patterns in a University Campus via Network Traffic AnalysisProceedings of the Asian Internet Engineering Conference 202410.1145/3674213.3674214(1-9)Online publication date: 9-Aug-2024
https://dl.acm.org/doi/10.1145/3674213.3674214
Wang YLyu MSivaraman V(2024)Standardizing Multimedia QoE Telemetry from Telecommunications Networks for Open AnalyticsProceedings of the 2024 SIGCOMM Workshop on Emerging Multimedia Systems10.1145/3672196.3673400(14-20)Online publication date: 4-Aug-2024
https://dl.acm.org/doi/10.1145/3672196.3673400

Index Terms

MetaVRadar: Measuring Metaverse Virtual Reality Network Activity
1. Networks
  1. Network performance evaluation
    1. Network measurement
  2. Network services
    1. Network management
    2. Network monitoring

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cover image Proceedings of the ACM on Measurement and Analysis of Computing Systems

Proceedings of the ACM on Measurement and Analysis of Computing Systems Volume 7, Issue 3

POMACS

December 2023

599 pages

EISSN:2476-1249

DOI:10.1145/3637453

Editors:
Augustin Chaintreau
Columbia University
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Leana Golubchik
University of Southern California, United States
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Zhi-Li Zhang
University of Minnesota, United States

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Copyright © 2023 ACM.

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Publication History

Published: 12 December 2023

Published in POMACS Volume 7, Issue 3

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Cited By

Lyu MWang YSivaraman V(2024)Measuring GenAI Usage Patterns in a University Campus via Network Traffic AnalysisProceedings of the Asian Internet Engineering Conference 202410.1145/3674213.3674214(1-9)Online publication date: 9-Aug-2024
https://dl.acm.org/doi/10.1145/3674213.3674214
Wang YLyu MSivaraman V(2024)Standardizing Multimedia QoE Telemetry from Telecommunications Networks for Open AnalyticsProceedings of the 2024 SIGCOMM Workshop on Emerging Multimedia Systems10.1145/3672196.3673400(14-20)Online publication date: 4-Aug-2024
https://dl.acm.org/doi/10.1145/3672196.3673400

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