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“元数据”需要前所未有的大量资源,特别是用于支持“扩展现实”的密集数据处理资源的计算资源,以及用于维持超高速和低纬度连接的巨大存储资源和庞大的网络资源。因此,这项工作旨在提出一个新的框架,即“元数据”,为管理和分配不同类型的用于“元数据”应用的资源提供一个非常有效和全面的解决办法。特别是,通过观察“元数据应用”可能具有共同功能,我们首先建议将应用程序分组成群,称为“元数据”。在“元数据”中,共同功能可以由应用程序共享。因此,同样的资源可以同时被多个应用程序使用,从而大大加强资源利用。为了解决实时特点和资源需求在“元数据”中的动态和不确定性,我们根据“半马尔科夫”决定程序开发了一个有效的框架,并提出了一个明智的入门控制算法,可以最大限度地利用资源,提高终端用户的服务质量。广泛的模拟结果表明,我们提议的解决方案在“Greedy”政策上优于“Greedy”应用,可以同时被多个应用程序使用,从而大大加强资源利用。为了解决实时特点和资源需求,在“Metversal”中分别要求接受80 %和47 %的长期收入。</s>