Adaptive SDN Autoscaling via Generalizable Multi-Agent Reinforcement Learning With EAGLE
TL;DR
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.
Abstract
Network automation and data-driven solutions are critical for modern network efficiency and resilience, especially as traditional manual management struggles to keep pace with increasing demands. The programmability of Software-Defined Networks (SDN) and their integration with Machine Learning (ML) algorithms have significantly advanced this quest, enabling data-driven network configuration. Within this context, autoscaling network resources is essential for efficiently operating softwarized and virtualized networks. However, current autoscaling solutions are limited by their logically centralized nature and their poor model portability across different network domains. To address these challenges, we propose EAGLE, a Multi-Agent Reinforcement Learning (MARL) system that autonomously orchestrates the scaling of network resources to meet flow demands and reduce power consumption. Powered by graph embedding, it generalizes to diverse network settings. We evaluate our solution over a Mininet-based emulator and assess its generalization capabilities on Fabric, a large-scale network testbed. Our results show that our approach can reduce flow completion time (FCT) by up to 40% while achieving up to 15% power savings. In addition, we show that our 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.