Policy-based QoS Control Using Call Admission Control and SVM

A call admission control algorithm using support vector machine (SVM) (SVM-CAC) is analyzed. SVM-CAC uses a service vector and a network vector to predict admission state. QoS metric function compares with some thresholds to determine the admission state. The threshold value can reveal biases of services. SVM-CAC combines policy and SVM´s advantages when making admission decisions, so it can take into account the business requirement, external network QoS resource and can reduce algorithm complexity. The simulation results show that this scheme accelerates calculation speed, has lower call delay, achieves better performance in terms of the call blocking probability and the call dropping probability than other machine learning admission control.