Flavio Esposito

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.