Flowlet-level Routing Optimization with GNN-based Multi-agent Deep Reinforcement Learning

Traditional flowlet routing is a traffic distributionbased network routing algorithm that avoids packet disorder problems. In dynamic network environments, however, this approach may lead to suboptimal performance and network congestion. In this paper, we design a multi-agent flowlet level routing optimization (MAFRO) framework that combines multi-agent deep reinforcement learning (MADRL) and graph neural networks (GNN). MADRL uses multiple agents that interact with the network environment and learn through a trial-and-error process to optimize flowlet routing. Meanwhile, MADRL uses the properties of GNN to model the network topology, interacting and capturing the complex relationships between network nodes. MAFRO enables agents to make more informed and adaptive routing decisions based on the current state of the network, leading to better end-to-end delay and packet loss rate performance. Experimental results demonstrate that MAFRO achieves better performance than the baseline algorithm.