The 6G network will support six major application scenarios, such as immersive communication, integrated AI and communication, and integrated sensing and communication. Many scenarios necessitate significant computational support. Moreover, user demands are becoming increasingly segmented, diverse, and personalized. Traditional network slicing alone is insufficient to meet the heterogeneous computing and networking demands of emerging service scenarios. Mobile computing network convergence (CNC) introduces a fundamentally different paradigm from the conventional cloud computing plus communication network model by deeply embedding computing resources into the mobile network infrastructure and enabling integrated computing-network services tailored to diverse user demands. In this article, we investigate orchestration architectures and mechanisms for CNC in 6G mobile networks. We begin by reviewing the evolution of CNC from a mobile network perspective and surveying existing studies, which we categorize according to mobile network architectures. Building on these insights, we propose a hierarchical, cross-domain coordination architecture and an orchestration mechanism based on hierarchical multi-agent reinforcement learning. Performance evaluations demonstrate that the proposed architecture and mechanism significantly reduce system energy consumption while enhancing task satisfaction rate. Finally, we discuss open challenges and future research directions.

翻译：6G网络将支持沉浸式通信、集成AI与通信、集成感知与通信等六大应用场景。许多场景需要大量计算支持。此外，用户需求日益细分、多样化和个性化。传统的网络切片已不足以满足新兴服务场景对异构计算和网络的需求。移动计算网络融合（CNC）通过将计算资源深度嵌入移动网络基础设施，并提供针对多样化用户需求的集成计算-网络服务，引入了一种与传统云计算加通信网络模型根本不同的范式。本文研究了6G移动网络中CNC的编排架构与机制。我们首先从移动网络视角回顾了CNC的演进，并对现有研究进行了分类（依据移动网络架构划分）。基于这些见解，我们提出了一种分层跨域协调架构及基于分层多智能体强化学习的编排机制。性能评估表明，所提出的架构与机制在显著提升任务满意率的同时降低了系统能耗。最后，我们讨论了开放性挑战与未来研究方向。