标题： 基于自适应人工智能的云边连续体去中心化资源管理 显示英文标题

标题： Adaptive AI-based Decentralized Resource Management in the Cloud-Edge Continuum

摘要： 随着应用需求的复杂性不断增加以及云边连续体的动态特性，对高效资源管理提出了重大挑战。 这些挑战源于不断变化的基础设施，其特点是节点和链路的添加、移除和重新配置，以及应用工作负载的可变性。 传统的集中式方法由于其静态特性难以适应这些变化，而分散式解决方案则面临全局可见性有限和协调开销大的挑战。 本文提出了一种用于动态应用部署和资源管理的混合分散框架。 该框架利用图神经网络（GNNs）来嵌入资源和应用状态，实现全面的表示和高效的决策。 它采用了一种协作式多智能体强化学习（MARL）方法，其中局部智能体在其邻域内优化资源管理，而全局编排器确保整个系统的协调。 通过将分散的应用部署与集中式监督相结合，我们的框架解决了云边连续体中固有的可扩展性、适应性和准确性挑战。 这项工作为分散式应用部署策略的发展、GNN嵌入的集成以及协作式MARL系统的构建做出了贡献，为高效、自适应和可扩展的资源管理提供了基础。 显示英文摘要

摘要： The increasing complexity of application requirements and the dynamic nature of the Cloud-Edge Continuum present significant challenges for efficient resource management. These challenges stem from the ever-changing infrastructure, which is characterized by additions, removals, and reconfigurations of nodes and links, as well as the variability of application workloads. Traditional centralized approaches struggle to adapt to these changes due to their static nature, while decentralized solutions face challenges such as limited global visibility and coordination overhead. This paper proposes a hybrid decentralized framework for dynamic application placement and resource management. The framework utilizes Graph Neural Networks (GNNs) to embed resource and application states, enabling comprehensive representation and efficient decision-making. It employs a collaborative multi-agent reinforcement learning (MARL) approach, where local agents optimize resource management in their neighborhoods and a global orchestrator ensures system-wide coordination. By combining decentralized application placement with centralized oversight, our framework addresses the scalability, adaptability, and accuracy challenges inherent in the Cloud-Edge Continuum. This work contributes to the development of decentralized application placement strategies, the integration of GNN embeddings, and collaborative MARL systems, providing a foundation for efficient, adaptive and scalable resource management.