Towards a Workload Trace Archive for Metaverse Systems

Recent years have seen a resurgence of societal interest in Metaverses and virtual reality (VR), with large companies such as Meta and Apple investing multi-billion dollars into its future. With the recent developments in VR hardware and software, understanding how to operate these systems efficiently and with good performance becomes increasingly important. However, studying Metaverse and VR systems is challenging because publicly available data detailing the performance of these systems is rare. Moreover, collecting this data is labor-intensive because VR devices are end-user devices that are driven by human input. In this work, we address this challenge and work towards a workload trace archive for Metaverse systems. To this end, we design, implement, and validate librnr, a system to record and replay human input on VR devices, automating large parts of the process of collecting VR traces. We use librnr to collect 106 traces with a combined runtime of 7 hours from state-of-the-art VR hardware under a variety of representative scenarios. Through analysis of our initial results, we find that power use of VR devices can increase by up to 29% depending on the location of the VR device relative to the user-defined play area, and show that noticeable performance degradation can occur when network bandwidth drops below 100Mbps. Encouraging community adoption of both librnr and the emerging trace archive, we publish both according to FAIR data principles at https://github.com/atlarge-research/librnr.