Quality-of-Service Performance Comparison: Machine Learning Regression and Classification-Based Predictive Routing Algorithm - M.A.Ridwana, N.A.M. Radzib, F. Abdullah, (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
The Internet network has evolved rapidly and by no means of slowing down. The complexity of the network is expected to grow exponentially, and with the high dependencies on Internet applications, there is a need to upgrade the current routing mechanism in the network. The conventional routing protocol that is based on the shortest path is no longer relevant. Recently, Machine Learning (ML) algorithms have become more prevalent in networking due to their ability to solve complex problems intelligently. This work proposes two ML predictive routing algorithms using regression and classification approaches to improve the Quality of Service in the network. Our simulation results show that the proposed regression-based routing improves the delay by up to 52% compared to the classification-based algorithm. Although the regression-based routing achieved better performance compared to the classification approach, it requires more input features to be trained. This work also discusses the pros and cons of both approaches.