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Nowadays, computer networks have always been complicated and difficult deployment for both the scientific and industry groups as they attempt to comprehend and analyze network performance as well as design efficient procedures for their operation. 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 thesis, we present a graph-based formulation of Abilene Network and other topologies 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. It is found that this model outperforms the average baseline predictor in predicting packet delay since the STGCN framework captures both spatial and temporal dimensions of the data.The evaluation is using STGCN to compare 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.More datasets are in used to verify and evaluate the performance of STGCN: 15 nodes network with various distributions and different network traffic distributions.More Machine Learning (ML) methods with lager network topologies are used for performance evaluation. STGCN outperforms the baseline methodology and other three techniques: Multiple Linear Regression (MLR), Extreme Gradient Boosting (XGBOOST) and RF in 15-node scale-free, 24-node GEANT2, and 50-node networks. Notably, our GNN-based methodology can achieve 97.0%, 95.9%, 96.1%, and 63.1% less root mean square error (RMSE) than the baseline predictor, MLR, XGBOOST and RF, respectively.All the experiments show that STGCN has good prediction performance with small and stable prediction errors. This thesis illustrates the feasibility and benefits of a GNN approach in predicting end-to-end delay in software-defined networks.

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Information-centric networking (ICN) has gained significant attention due to its in-network caching and named-based routing capabilities. Caching plays a crucial role in managing the increasing network traffic and improving the content delivery efficiency. However, caching faces challenges as routers have limited cache space while the network hosts tens of thousands of items. This paper focuses on enhancing the cache performance by maximizing the cache hit ratio in the context of software-defined networking–ICN (SDN-ICN). We propose a statistical model that generates users’ content preferences, incorporating key elements observed in real-world scenarios. Furthermore, we introduce a graph neural network–double deep Q-network (GNN-DDQN) agent to make caching decisions for each node based on the user request history. Simulation results demonstrate that our caching strategy achieves a cache hit ratio 34.42% higher than the state-of-the-art policy. We also establish the robustness of our approach, consistently outperforming various benchmark strategies.