Socially-Aware Traffic Scheduling for Edge-Assisted Metaverse by Deep Reinforcement Learning
Ao Yu, Hui Yang, Cuiyang Feng, Yunbo Li, Yang Zhao, Mohamed Cheriet, Athanasios V. Vasilakos
Abstract
The metaverse presents a new Internet paradigm where individuals can engage in activities like play, work, and social interactions in an immersive virtual world. Empowered by edge computing technologies, users can seamlessly delve into this digital universe through ultra-fast rendering of virtual environments and efficient storage of character data. However, the large-scale social interaction of the metaverse could strain network bandwidth, exacerbating congestion on the existing edge computing framework. To address these challenges, we consider a human-centric edge collaboration architecture that allows edge nodes in the same social activities can collaborate to meet user needs. Moreover, we introduce a socially-aware traffic scheduling algorithm within this architecture to address the complex cross-edge resource allocation problem. By formulating the traffic scheduling problem as a Markov Decision Process (MDP) considering both network and social factors, we present a novel socially-aware deep reinforcement learning (DRL) algorithm tailored for this MDP. Extensive experiments demonstrate that our approach satisfies network performance during extreme burst traffic scenarios, effectively managing the dynamic social demands of the metaverse.