GNN-based End-to-end Delay Prediction in Software Defined Networking
Zhun Ge, Jiacheng Hou, Amiya Nayak
Abstract
In software-defined networking (SDN), predicting latency (delay) is essential for enhancing performance, power consumption and resource utilization in meeting its significant latency requirements. In this paper, we present a graph-based formulation of Abilene Network and apply a Graph Neural Network (GNN)-based model, Spatial-Temporal Graph Convolutional Network (STGCN), to predict end-to-end packet delay on this formulation. We find this model outperforms the average baseline predictor in predicting packet delay since the STGCN framework captures both spatial and temporal dimensions of the data. We also compare STGCN with other machine learning methods: Random Forest (RF) and Neural Network (NN). In the most complex network traffic condition with high traffic intensity, varying capacities and propagation delay, STGCN is 68.5% and 78.7% better than RF and NN, respectively. This illustrates the feasibility and benefits of a GNN approach in predicting end-to-end delay in software-defined networks.