Conditioned Functional Limits and Applications to Queues

TL;DR

This paper studies the asymptotic behavior of renewal processes conditioned on the number of events, showing convergence to a Brownian bridge and applying these results to queue performance analysis.

Contribution

It establishes functional limit theorems for conditioned renewal processes and demonstrates their application to queue workload approximations.

Findings

Conditioned renewal processes converge to a Brownian bridge.

Queue workload can be approximated by a reflected diffusion with Brownian bridge input.

Results extend to exchangeable random variables and involve martingale techniques.

Abstract

We consider a renewal process that is conditioned on the number of events in a fixed time horizon. We prove that a centered and scaled version of this process converges to a Brownian bridge, as the number of events grows large, which relies on first establishing a functional strong law of large numbers result to determine the centering. These results are consistent with the asymptotic behavior of a conditioned Poisson process. We prove the limit theorems over triangular arrays of exchangeable random variables, obtained by conditionning a sequence of independent and identically distributed renewal processes. We construct martingale difference sequences with respect to these triangular arrays, and use martingale convergence results in our proofs. To illustrate how these results apply to performance analysis in queueing, we prove that the workload process of a single server queue with conditioned renewal arrival process can be approximated by a reflected diffusion having the sum of a Brownian Bridge and Brownian motion as input to its regulator mapping.