A large deviations analysis of the transient of a queue with many Markov fluid inputs

Abstract

This article analyzes the transient buffer content distribution of a queue fed by a large number of Markov fluid sources. We characterize the probability of overflow at time t , given the current buffer level and the number of sources in the on-state. After scaling buffer and bandwidth resources by the number of sources n , we can apply large deviations techniques. The transient overflow probability decays exponentially in n . In case of exponential on/off sources, we derive an expression for the decay rate of the rare event probability under consideration. For general, Markov fluid sources, we present a plausible conjecture. We also provide the "most likely path" from the initial state to overflow (at time t ). Knowledge of the decay rate and the most likely path to overflow leads to (i) approximations of the transient overflow probability, and (ii) efficient simulation methods of the rare event of buffer overflow. The simulation methods, based on importance sampling, give a huge speed-up compared to straightforward simulations. The approximations are of low computational complexity, and accurate, as verified by means of simulation experiments.

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