Adaptive AI-based decentralized resource management in the cloud-edge continuum

Li L., Bell J., Coppola M., Lomonaco V.

AI  Continuum Computing  Decentralized Application Placement  Dynamic Resource Management  Multi-Agent Reinforcement Learning 

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.

Publisher: Institute of Electrical and Electronics Engineers Inc.

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