In the Cloud-Edge Continuum, dynamic infrastructure change and variable workloads complicate efficient resource management. Centralized methods can struggle to adapt, whilst purely decentralized policies lack global oversight. This paper proposes a hybrid framework using Graph Neural Network (GNN) embeddings and collaborative multi-agent reinforcement learning (MARL). Local agents handle neighbourhood-level decisions, and a global orchestrator coordinates system-wide. This work contributes to decentralized application placement strategies with centralized oversight, GNN integration and collaborative MARL for efficient, adaptive and scalable resource management.

翻译：在云边连续体中，动态的基础设施变化和可变的工作负载使得高效的资源管理变得复杂。集中式方法难以适应这些变化，而纯粹的去中心化策略则缺乏全局监管。本文提出了一种混合框架，利用图神经网络嵌入和协作式多智能体强化学习。本地智能体处理邻域级决策，全局编排器则协调整个系统。这项工作通过集中式监管、图神经网络集成以及协作式多智能体强化学习，为去中心化应用部署策略做出了贡献，实现了高效、自适应和可扩展的资源管理。