Adaptive QoS Protocol in Systemized Networks with Machine Learning
K P Nitish Rao, Sangeeth Kumar, K Keerthika · 2024
In today’s networking world, Quality of Service (QoS) is essential for guaranteeing that data packets are delivered efficiently. Understanding metrics like jitter, throughput, and latency is also important for meeting the needs of different users and applications. As a result, QoS techniques have a direct impact on the network and may be overly inflexible in an operator-centric setting. This article describes a revolutionary approach to network QoS management based on machine learning (ML) algorithms such as KNN, random forest, and decision trees. In this research, NS3 simulations and real-world data are used to train and test the machine learning models on several QoS metrics, demonstrating their automation potential. The findings of this study pave the door for dynamic QoS adjustment, which would enhance service delivery.