Tails in Monotone-Separable Stochastic Networks

Abstract

A network belongs to the monotone separable class if its state variables are homogeneous and monotone functions of the epochs of the arrival process. This framework, which was first introduced to derive the stability region for stochastic networks with stationary and ergodic driving sequences, is revisited. It contains several classical queueing network models, including generalized Jackson networks, max plus networks, multiserver queues, and various classes of stochastic Petri nets. Our purpose is the analysis of the tails of the stationary state variables in the particular case of i.i.d. driving sequences. For this, we establish general relationships between networks of this class and the GI/GI/1/∞ queue. We show that under sub-exponential assumptions for service times, the stationary maximal dater in any such network (typically the time to empty the network when stopping further arrivals) has tail asymptotics which can be bounded from below and from above by a multiple of the second tails of service times. In general, the upper and the lower bounds do not coincide. Nevertheless, exact asymptotics can be obtained along the same lines for various special cases of networks, providing direct extensions of Veraverbeke's tail asymptotic for the stationary waiting times in the GI/GI/1/∞ queue. We exemplify this on tandem queues (maximal daters and delays in stations) as well as on multiserver queues. This methodology for exact asymptotics can be extended to other classes of monotone separable networks like general reducible max plus networks, or generalized Jackson networks.