Creating and maintaining the Metaverse requires enormous resources that have never been seen before, especially computing resources for intensive data processing to support the Extended Reality, enormous storage resources, and massive networking resources for maintaining ultra high-speed and low-latency connections. Therefore, this work aims to propose a novel framework, namely MetaSlicing, that can provide a highly effective and comprehensive solution in managing and allocating different types of resources for Metaverse applications. In particular, by observing that Metaverse applications may have common functions, we first propose grouping applications into clusters, called MetaInstances. In a MetaInstance, common functions can be shared among applications. As such, the same resources can be used by multiple applications simultaneously, thereby enhancing resource utilization dramatically.To address the real-time characteristic and resource demand's dynamic and uncertainty in the Metaverse, we develop an effective framework based on the semi-Markov decision process and propose an intelligent admission control algorithm that can maximize resource utilization and enhance the Quality-of-Service for end-users. Extensive simulation results show that our proposed solution outperforms the Greedy-based policies by up to 80% and 47% in terms of long-term revenue for Metaverse providers and request acceptance probability, respectively.

翻译：创建和维护元宇宙需要前所未有的巨大资源，特别是支持扩展现实（Extended Reality）所需的密集计算资源、庞大的存储资源，以及维持超高速低延迟连接的海量网络资源。为此，本文提出一种名为MetaSlicing的新型框架，为元宇宙应用提供高效全面的资源管理与分配解决方案。具体而言，通过观察元宇宙应用可能具备共同功能，我们首先提出将应用聚类成组，称为MetaInstances（元实例）。在MetaInstance中，应用间可共享通用功能，从而使同一资源能被多个应用同时使用，显著提升资源利用率。为应对元宇宙的实时性特征及资源需求的动态性与不确定性，我们基于半马尔可夫决策过程开发了一套有效框架，并提出一种智能接纳控制算法，该算法可最大化资源利用率并提升终端用户的服务质量。大量仿真结果表明，与基于贪婪策略的方案相比，我们提出的方案在元宇宙供应商的长期收益方面提升了高达80%，在请求接受概率方面提升了高达47%。