Evaluating Network Scalability of Metaverse-Applicable Use Cases

Abstract

Within the context of scalability for the Metaverse, the network remains a principal limiting factor even if extended reality adoption were to increase, given large volumes of data needed to support complex use cases. This thesis introduces a systems framework to evaluate the scalability of network architecture within the Metaverse, envisioned as the next generation of 3D-enabled internet. Through two experiments, we developed a model to determine whether various Metaverse use cases could be supported by current network infrastructure. The first experiment utilized Meta's Horizon Worlds platform to assess the throughput scalability of objects. The second experiment constructed a model categorizing use cases and evaluated their expected throughput against current data rates, incorporating data from the first experiment and existing literature. The findings indicate that static objects do not contribute persistent throughput, while moving objects exhibit an approximate throughput of 25 Kbps each. Furthermore, education, entertainment, facility design, product design, and training are identified as use case categories most constrained by current infrastructure capabilities.