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This paper presents models, algorithms and analysis for measurement-based admission control in network applications in which there is high uncertainty concerning source statistics. In the process it extends and unifies several recent approaches to admission control. A new class of algorithms is introduced based on results concerning Markov's canonical distributions. In addition, a new model is developed for the evolution of the number of flows in the admission control system. Performance evaluation is done through both analysis and simulation. Results show that the proposed algorithms minimize buffer-overflow probability among the class of all moment-consistent algorithms. Index Terms--Canonical distributions, measurement-based admission control, robust estimation, worst case source models.

This paper presents models, algorithms and analysis for measurement-based admission control in network applications in which there is high uncertainty concerning source statistics. In the process it extends and unifies several recent approaches to admission control. A new class of algorithms is introduced based on results concerning Markov's canonical distributions. In addition, a new model is developed for the evolution of the number of flows in the admission control system. Performance evaluation is done through both analysis and simulation. Results show that the proposed algorithms minimize buffer-overflow probability among the class of all moment-consistent algorithms. [ABSTRACT FROM AUTHOR]