Quality-of-Service Performance Comparison: Machine Learning Regression and Classification-Based Predictive Routing Algorithm

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M.A.Ridwana, N.A.M. Radzib, F. Abdullah


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.

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