Dynamic Resource Allocation for Metaverse Applications with Deep Reinforcement Learning

【Author】 Chu, Nam H.; Nguyen, Diep N.; Hoang, Dinh Thai; Phan, Khoa T.; Dutkiewicz, Eryk; Niyato, Dusit; Shu, Tao

【Source】2023 IEEE WIRELESS COMMUNICATIONS AND NETWORKING CONFERENCE, WCNC

【影响因子】

【Abstract】This work proposes a novel framework to dynamically and effectively manage and allocate different types of resources for Metaverse applications, which are forecasted to demand massive resources of various types that have never been seen before. Specifically, by studying functions of Metaverse applications, we first propose an effective solution to divide applications into groups, namely MetaInstances, where common functions can be shared among applications to enhance resource usage efficiency. Then, to capture the real-time, dynamic, and uncertain characteristics of request arrival and application departure processes, we develop a semi-Markov decision processbased framework and propose an intelligent algorithm that can gradually learn the optimal admission policy to maximize the revenue and resource usage efficiency for the Metaverse service provider and at the same time enhance the Quality-of-Service for Metaverse users. Extensive simulation results show that our proposed approach can achieve up to 120% greater revenue for the Metaverse service providers and up to 178.9% higher acceptance probability for Metaverse application requests than those of other baselines.

【Keywords】Metaverse; deep reinforcement learning; semi-Markov decision process; network slicing

【发表时间】2023

【收录时间】2023-06-25

【文献类型】理论模型

【主题类别】

区块链应用-虚拟经济-元宇宙