  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". 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
method MetaVRadar to detect metaverse session and classify the user activity
state leveraging formalized flow signatures and volumetric attributes. Our
third contribution practically implements MetaVRadar, 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.
