Compute- and Data-Intensive Networks: The Key to the Metaverse
Compute- and Data-Intensive Networks: The Key to the Metaverse
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DOI：
10.48550/arxiv.2204.02001
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发表时间：
2022-04
期刊：
影响因子：
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通讯作者：
Yang Cai;Jaime Llorca;A. Tulino;A. Molisch
中科院分区：
文献类型：
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作者：
Yang Cai;Jaime Llorca;A. Tulino;A. Molisch
长期以来，计算、通信和存储的世界一直被分开对待，即使是云计算、分布式计算和移动的边缘计算的最新趋势也没有从根本上改变网络的角色，网络仍然被设计为在终端用户和预定的计算节点之间移动数据，而没有真正优化端到端的计算通信过程。然而，Metaverse应用程序的出现，其中用户消费来自分布式实时源和存储的数字资产的实时组合的多媒体体验，已经改变了对提供分布式缓存，计算和通信的系统的要求和可能性。我们认为，Metaverse应用程序的实时交互性和对数据存储、流传输速率和处理能力的高要求将加速云与网络的融合，从而使高度分布式、紧密集成的计算和数据密集型网络成为下一代数字体验的通用计算平台。在本文中，我们首先描述了Metaverse应用程序和相关支持基础设施的需求，包括相关的用例。然后，我们概述了一个全面的云网络流数学框架，该框架旨在对此类系统进行端到端的优化和控制，并展示了数值结果，说明了其对Metaverse就绪网络的有效运行的重要作用。
The worlds of computing, communication, and storage have for a long time been treated separately, and even the recent trends of cloud computing, distributed computing, and mobile edge computing have not fundamentally changed the role of networks, still designed to move data between end users and pre-determined computation nodes, without true optimization of the end-to-end compute-communication process. However, the emergence of Metaverse applications, where users consume multimedia experiences that result from the real-time combination of distributed live sources and stored digital assets, has changed the requirements for, and possibilities of, systems that provide distributed caching, computation, and communication. We argue that the real-time interactive nature and high demands on data storage, streaming rates, and processing power of Metaverse applications will accelerate the merging of the cloud into the network, leading to highly-distributed tightly-integrated compute- and data-intensive networks becoming universal compute platforms for next-generation digital experiences. In this paper, we first describe the requirements of Metaverse applications and associated supporting infrastructure, including relevant use cases. We then outline a comprehensive cloud network flow mathematical framework, designed for the end-to-end optimization and control of such systems, and show numerical results illustrating its promising role for the efficient operation of Metaverse-ready networks.