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

Lanpei Li, Jack Bell, Massimo Coppola, Vincenzo Lomonaco

Abstract: 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.

External IDs:dblp:conf/pdp/LiBCL25