Adaptive AI-based Decentralized Resource Management in the Cloud-Edge Continuum
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 9 of its 33 references that are in the map. First posted on arXiv 27 Jan 2025.
Builds on
9 of 33 references.
References · 9
- 2017Inductive Representation Learning on Large Graphscited by 6,284
- 2020Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learningcited by 41
- 2024Graph Neural Networks and Deep Reinforcement Learning Based Resource Allocation for V2X Communicationscited by 18
- 2018Resource Management in Fog/Edge Computing: A Surveycited by 13
- 2019YAFS: A simulator for IoT scenarios in fog computingcited by 8
- 2024SPQR: Controlling Q-ensemble Independence with Spiked Random Model for Reinforcement Learningcited by 8
- 2018The Internet of Things, Fog and Cloud Continuum: Integration and Challengescited by 6
- 2020EdgeSlice: Slicing Wireless Edge Computing Network with Decentralized Deep Reinforcement Learningcited by 6
- 2017Socio-technical Smart Grid Optimization via Decentralized Charge Control of Electric Vehiclescited by 5
Built on by
Citing papers per year: 2025: 1.
1 paper.