A Decision Theoretic Approach to Measurement-based Admission Control

This paper presents a new approach to measurement-based admission control (MBAC) using real-time traffic classification. The network traffic is classified based on Bayesian decision theory. We derive the decision rules and cost functions considering utilization, packet loss rate, and rejection rate. The approach is compared by simulations against other measurement-based admission control approaches. Experiments show that the new method offers significant advantages particularly when the traffic is highly dynamic or complex.