A unified approach to linear programming bounds for queueing networks: systems with polyhedral invariance of transition probabilities
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abstract
We develop a framework for obtaining linear programming performance bounds in queueing networks. The structure allowing for the development of the bounds requires that the underlying Markov chain model possess translational invariance of its transition probabilities on polyhedra. Such a structure is exhibited by many systems of interest. The bounds are then obtained via a performance-to-performance duality.
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Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187)