Network Latency Classification for Computer Games

Wong, Albert; Chiu, Chunyin; Hains, Gaétan; Behnke, James; Khmelevsky, Youry; Sutherland, Tyler

doi:10.1109/rasse53195.2021.9686848

Cited by 4 publications

(1 citation statement)

References 22 publications

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“…Many prior works analyze network traffic characteristics of specific networked applications for detection and performance optimization, such as streaming video [23], 360-degree video [47], online conferencing [12,31], WiFi voice calls [33], TLS usage of IoT devices [39], and online games [32,46]. The work in [32] focuses on detecting gameplay on PC platforms by analyzing packet byte signatures in gaming UDP flows, which serves as an inspiration for our method of detecting time-critical activity domain flows.…”

Section: Related Workmentioning

confidence: 99%

MetaVRadar: Measuring Metaverse Virtual Reality Network Activity

Lyu,

Tripathi,

Sivaraman

2023

Proc. ACM Meas. Anal. Comput. Syst.

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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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