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Doriana Monaco

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Open access 2026

Adaptive SDN Autoscaling via Generalizable Multi-Agent Reinforcement Learning With EAGLE

This work proposes EAGLE, a Multi-Agent Reinforcement Learning (MARL) system that autonomously orchestrates the scaling of network resources to meet flow demands and reduce power consumption, and shows that the trained model can “zero-shot generalize” to unseen network topologies that share structural or statistical similarity with the training domain, hence reducing training time and associated energy costs.

Doriana Monaco, Alessio Sacco, Flavio Esposito et al. · 0 citations

Testing the Divergence Stack Memory on GPGPUs: A Modular in-Field Test Strategy

RL-ICE is proposed as an innovative scheduler that can work in such a cloud continuum by leveraging a multi-cluster and hierarchical RL to satisfy both user Quality of Experience (QoE) metrics and tenant’s costs.

Doriana Monaco, Alessio Sacco, C. Casetti et al. · 0 citations