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 process-based 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.

翻译：本文提出了一种新颖框架，用于动态且高效地管理和分配元宇宙应用所需的各类资源。据预测，这些应用将对前所未有的多种类型资源产生巨大需求。具体而言，通过研究元宇宙应用的功能，我们首先提出了一种有效方案，将应用划分为若干组（即MetaInstances），使组内通用功能可在应用间共享，从而提升资源利用效率。接着，为捕捉请求到达与应用离开过程的实时性、动态性和不确定性，我们开发了基于半马尔可夫决策过程的框架，并提出了一种智能算法，该算法能逐步学习最优接纳策略，以最大化元宇宙服务提供商的收益和资源利用效率，同时提升元宇宙用户的服务质量。大量仿真实验表明，与现有基线方法相比，我们提出的方法可使元宇宙服务提供商的收益提升高达120%，元宇宙应用请求的接纳概率提升高达178.9%。