论文标题
跨度:元元的新型资源分配框架
MetaSlicing: A Novel Resource Allocation Framework for Metaverse
论文作者
论文摘要
创建和维护元评估需要以前从未见过的庞大资源,尤其是计算用于密集数据处理的资源,以支持扩展现实,庞大的存储资源以及大量的网络资源,以维持超高速和低延迟连接。因此,这项工作旨在提出一个新颖的框架,即,可以为管理和分配不同类型的资源用于元应用程序提供高效且全面的解决方案。特别是,通过观察元应用程序可能具有共同的功能,我们首先将应用程序分组到群集中,即称为元音。在元结构中,可以在应用程序之间共享共同的功能。因此,可以同时使用多个应用程序来使用相同的资源,从而大大增强资源利用率。为了解决元元中的实时特征和资源需求的动态和不确定性,我们基于半马克的决策过程,并提出一个有效的框架,并提出一个智能的录取控制算法,可以最大限度地利用资源利用,以提高质量的质量,以实现质量的质量,并以良好的群体为生。广泛的仿真结果表明,我们提出的解决方案在元提供者的长期收入方面,基于贪婪的政策的表现高达80%和47%,并要求接受概率。
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.