Large deviations for acyclic networks of queues with correlated Gaussian inputs

TL;DR

This paper analyzes the probability decay rate of large queue lengths in acyclic queue networks fed by correlated Gaussian inputs, deriving bounds and conditions for exact asymptotic decay rates.

Contribution

It provides a large deviations analysis for acyclic queue networks with correlated Gaussian inputs, establishing bounds and conditions for the decay rate of overflow probabilities.

Findings

Lower bound for decay rate derived using Schilder's theorem.

Bound is tight under certain technical conditions.

Decay rates match those of isolated queues with Gaussian inputs under specific correlations.

Abstract

We consider an acyclic network of single-server queues with heterogeneous processing rates. It is assumed that each queue is fed by the superposition of a large number of i.i.d. Gaussian processes with stationary increments and positive drifts, which can be correlated across different queues. The flow of work departing from each server is split deterministically and routed to its neighbors according to a fixed routing matrix, with a fraction of it leaving the network altogether. We study the exponential decay rate of the probability that the steady-state queue length at any given node in the network is above any fixed threshold, also referred to as the "overflow probability". In particular, we first leverage Schilder's sample-path large deviations theorem to obtain a general lower bound for the limit of this exponential decay rate, as the number of Gaussian processes goes to infinity. Then, we show that this lower bound is tight under additional technical conditions. Finally, we show that if the input processes to the different queues are non-negatively correlated, non short-range dependent fractional Brownian motions, and if the processing rates are large enough, then the asymptotic exponential decay rates of the queues coincide with the ones of isolated queues with appropriate Gaussian inputs.