a framework for robust measurement-based admission control
Measurement-based Admission Control (MBAC) is an attractive mechanism to concurrently offer Quality of Service (QoS) to users, without requiring a-priori traffic specification and on-line policing. However, several aspects of such a system need to be clearly understood in order to devise robust MBAC schemes. Through a sequence of increasingly sophisticated stochastic models, we study the impact of parameter estimation errors, of flow arrival and departure dynamics, and of estimation memory on the performance of an MBAC system.We show that a certainty equivalence assumption, i.e., assuming that the measured parameters are the real ones, can grossly compromise the target performance of the system. We quantify the improvement in performance as a function of the memory size of the estimator and a more conservative choice of the certainty-equivalent parameters. Our results yield valuable new insight into the performance of MBAC schemes, and represent quantitative guidelines for the design of robust schemes.
robust measurement admission control  framework 