{"title":"媒体融合 Metaverse 中的人际交流互联","authors":"Xin Wang, Jianhui Lv, Achyut Shankar, Carsten Maple, Keqin Li, Qing Li","doi":"10.1145/3670998","DOIUrl":null,"url":null,"abstract":"<p>The metaverse aims to provide immersive virtual worlds connecting with the physical world. To enable real-time interpersonal communications between users across the globe, the metaverse places high demands on network performance, including low latency, high bandwidth, and fast network speeds. This paper proposes a novel Media Convergence Metaverse Network (MCMN) framework to address these challenges. Specifically, the META controller serves as MCMN's logically centralized control plane, responsible for holistic orchestration across edge sites and end-to-end path computation between metaverse users. We develop a model-free deep reinforcement learning-based metaverse traffic optimization algorithm that learns to route flows while satisfying the Quality of Service (QoS) boundaries. The network slicing engine leverages artificial intelligence and machine learning to create isolated, customized virtual networks tailored for metaverse traffic dynamics on demand. It employs unsupervised and reinforcement learning techniques using network telemetry from the META controller to understand application traffic patterns and train cognitive slicer agents to make quality of service -aware decisions accordingly. Optimized delivery of diverse concurrent media types necessitates routing intelligence to meet distinct requirements while mitigating clashes over a shared infrastructure. Media-aware routing enhances traditional shortest-path approaches by combining topological metrics with workflow sensitivities. We realize an edge-assisted rendering fabric to offload complex processing from bandwidth-constrained endpoints while retaining visual realism. Extensive simulations demonstrate MCMN's superior performance compared to conventional networking paradigms. 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引用次数: 0
摘要
元宇宙旨在提供与物理世界相连接的身临其境的虚拟世界。为了实现全球用户之间的实时人际交流,元宇宙对网络性能提出了很高的要求,包括低延迟、高带宽和高速网络。本文提出了一个新颖的媒体融合元宇宙网络(MCMN)框架来应对这些挑战。具体来说,META 控制器作为 MCMN 的逻辑集中控制平面,负责边缘站点之间的整体协调以及元网络用户之间的端到端路径计算。我们开发了一种基于无模型深度强化学习的元数据流量优化算法,该算法可在满足服务质量(QoS)边界的前提下学习流量路由。网络切片引擎利用人工智能和机器学习来创建隔离的、定制的虚拟网络,以满足元数据流量动态需求。它利用来自 META 控制器的网络遥测数据,采用无监督和强化学习技术来了解应用流量模式,并训练认知切片代理做出相应的服务质量感知决策。优化各种并发媒体类型的传输需要路由智能,以满足不同的要求,同时减少共享基础设施上的冲突。媒体感知路由通过将拓扑指标与工作流敏感性相结合,增强了传统的最短路径方法。我们实现了边缘辅助渲染结构,以便从带宽受限的端点卸载复杂的处理过程,同时保持视觉的真实感。大量的仿真证明,与传统网络范例相比,MCMN 的性能更加卓越。MCMN 在实现无缝互联和超高保真通信以释放元宇宙的真正潜力方面大有可为。
Interpersonal Communication Interconnection in Media Convergence Metaverse
The metaverse aims to provide immersive virtual worlds connecting with the physical world. To enable real-time interpersonal communications between users across the globe, the metaverse places high demands on network performance, including low latency, high bandwidth, and fast network speeds. This paper proposes a novel Media Convergence Metaverse Network (MCMN) framework to address these challenges. Specifically, the META controller serves as MCMN's logically centralized control plane, responsible for holistic orchestration across edge sites and end-to-end path computation between metaverse users. We develop a model-free deep reinforcement learning-based metaverse traffic optimization algorithm that learns to route flows while satisfying the Quality of Service (QoS) boundaries. The network slicing engine leverages artificial intelligence and machine learning to create isolated, customized virtual networks tailored for metaverse traffic dynamics on demand. It employs unsupervised and reinforcement learning techniques using network telemetry from the META controller to understand application traffic patterns and train cognitive slicer agents to make quality of service -aware decisions accordingly. Optimized delivery of diverse concurrent media types necessitates routing intelligence to meet distinct requirements while mitigating clashes over a shared infrastructure. Media-aware routing enhances traditional shortest-path approaches by combining topological metrics with workflow sensitivities. We realize an edge-assisted rendering fabric to offload complex processing from bandwidth-constrained endpoints while retaining visual realism. Extensive simulations demonstrate MCMN's superior performance compared to conventional networking paradigms. MCMN shows great promise to enable seamless interconnectivity and ultra-high fidelity communications to unlock the true potential of the metaverse.
期刊介绍:
ACM Transactions on Internet Technology (TOIT) brings together many computing disciplines including computer software engineering, computer programming languages, middleware, database management, security, knowledge discovery and data mining, networking and distributed systems, communications, performance and scalability etc. TOIT will cover the results and roles of the individual disciplines and the relationshipsamong them.