Compute- and Data-Intensive Networks: The Key to the Metaverse
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Abstract (arXiv)
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.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Apr 2022.
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Citing papers per year: 2022: 2; 2023: 3; 2024: 1; 2025: 1; 2026: 2.
9 papers.
- 2022Edge Computing for Semantic Communication Enabled Metaverse: An Incentive Mechanism Designcited by 4
- 2023The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directionscited by 3
- 2023Human-Centric Resource Allocation for the Metaverse With Multiaccess Edge Computingcited by 3
- 2023Dynamic Partial Computation Offloading for the Metaverse in In-Network Computingcited by 1
- 2022Need of 6G for the Metaverse Realizationcited by 0
- 2024Metaverse Survey & Tutorial: Exploring Key Requirements, Technologies, Standards, Applications, Challenges, and Perspectivescited by 0
- 2025A Flexible Multi-Agent Deep Reinforcement Learning Framework for Dynamic Routing and Scheduling of Latency-Critical Servicescited by 0
- 2026A Constrained RL Approach for Cost-Efficient Delivery of Latency-Sensitive Applicationscited by 0
- 2026From Prior-Guided Heuristics to Deployable Agents: Accelerating Demonstration-Driven Reinforcement Learning for Deadline-Constrained Network Controlcited by 0
- 2026From Prior-Guided Heuristics to Deployable Agents: Accelerating Demonstration-Driven Reinforcement Learning for Deadline-Constrained Network Control3 Sep 2026
- 2026A Constrained RL Approach for Cost-Efficient Delivery of Latency-Sensitive Applications4 Mar 2026
- 2025A Flexible Multi-Agent Deep Reinforcement Learning Framework for Dynamic Routing and Scheduling of Latency-Critical Services13 Oct 2025
- 2024Metaverse Survey & Tutorial: Exploring Key Requirements, Technologies, Standards, Applications, Challenges, and Perspectives7 May 2024
- 2023Human-Centric Resource Allocation for the Metaverse With Multiaccess Edge Computing23 Dec 2023
- 2023Dynamic Partial Computation Offloading for the Metaverse in In-Network Computing9 Jun 2023
- 2023The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions18 Apr 2023
- 2022Need of 6G for the Metaverse Realization28 Dec 2022
- 2022Edge Computing for Semantic Communication Enabled Metaverse: An Incentive Mechanism Design13 Dec 2022