ML-based pre-deployment SDN performance prediction with neural network boosting regression

Abstract

Software defined networking (SDN) has been proposed as an effective approach to improve network management efficiency and increase network intelligence in various networks. However, configuring and deploying suitable SDN functionalities is challenging. To overcome these obstacles in SDN network deployment, machine learning models have been introduced to monitor and predict the quality of service (QoS) in an SDN-enabled network. However, most existing studies are applicable only to deployed SDN networks with specific network topologies. In this study, a prediction-based SDN network configuration and deployment scheme is proposed for unseen network topologies before the actual deployment of SDN functionalities. In addition, a machine learning-based pre-deployment SDN performance prediction problem is formulated, and a neural network boosting regression model (i.e., NNBoost) is proposed as the solution. Numerical experiments demonstrate that NNBoost outperforms other machine learning algorithms, including deep learning methods, proposed in the literature, on datasets generated with both real-world and synthetic network topologies, compared with random forest (RF), AdaBoost, XGBoost, LightGBM, deep neural network (DNN) and long short-term memory (LSTM). It is found that NNBoost achieves lower root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) results for the maximum and mean values for the Round-Trip Time (RTT), Switch-to-Controller (S2C) traffic, and Controller-to-Controller (C2C) traffic. It is also found that by adding extra training data samples generated with synthetic network topologies, NNBoost achieves a better prediction performance on the test data samples for real-world network topologies.