Performance of Multiclass Markovian Queueing Networks Via Piecewise Linear Lyapunov Functions

queueing networks under a stable policy. We propose a general methodology based on Lyapunov functions for the performance analysis of infinite state Markov chains and apply it specifically to Markovian multiclass queueing networks. We establish a deeper connection between stability and performance of such networks by showing that if there exist linear and piecewise linear Lyapunov functions that show stability, then these Lyapunov functions can be used to establish geometric-type lower and upper bounds on the tail probabilities, and thus bounds on the expectation of the queue lengths. As an example of our results, for a reentrant line queueing network with two processing stations operating under a work-conserving policy, we

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