Research on Generalized Intelligent Routing Technology Based on Graph Neural Network

Abstract

Aiming at the problems of poor load balancing ability and weak generalization of the existing routing algorithms, this paper proposes an intelligent routing algorithm, GNN-DRL, in the Software Defined Networking (SDN) environment. The GNN-DRL algorithm uses a graph neural network (GNN) to perceive the dynamically changing network topology, generalizes the state of nodes and edges, and combines the self-learning ability of Deep Reinforcement Learning (DRL) to find the optimal routing strategy, which makes GNN-DRL minimize the maximum link utilization and reduces average end-to-end delay under high network load. In this paper, the GNN-DRL intelligent routing algorithm is compared with the Open Shortest Path First (OSPF), Equal-Cost Multi-Path (ECMP), and intelligence-driven experiential network architecture for automatic routing (EARS). The experimental results show that GNN-DRL reduces the maximum link utilization by 13.92% and end-to-end delay by 9.48% compared with the superior intelligent routing algorithm EARS under high traffic load, and can be effectively extended to different network topologies, making possible better load balancing capability and generalizability.

DQN-GNN-Based User Association Approach for Wireless Networks

“…Combining DRL and GNN can enable the development of more advanced intelligent systems that can reason and act in complex environments with graph-structured data [16]. By using GNNs to encode and process graph-structured data, DRL agents can better understand the relationships between entities in their environment, allowing them to make more informed decisions and take actions that lead to better performance [17].…”

Research Status and Prospect of Medical Material Scheduling

“…Finally, the dynamic scheduling scheme of materials under the condition of extreme shortage of rescue vehicles is obtained. In view of the uncertainty and complexity of traffic flow in the process of medical materials transportation, Huang Chengning, Li Juan and others [18] A graph neural network is proposed to solve the problem of material scheduling time series prediction. He Tilong, Lou Wengao [19] This paper studies the emergency material scheduling problem from multiple rescue points to multiple demand points under three kinds of road damage conditions, establishes a model with the goal of minimizing the total material loading time and the minimum consumption cost, and solves the model with an improved moth extinguishing algorithm.…”