Indexed metadata

Volterra-Skorokhod Reflection and Fluid Limits of Loss Queues

Mingrui Wang, Prakash Chakraborty

Source record

Source: arXiv

Published: Oct 15, 2026

arXiv: 2610.22607

Open original source ↗

Source abstract

In the Gt/G/n/nG_t/G/n/n loss queue, a customer blocked at time uu remains missing from the system for as long as it would have been served. A fraction K(t−u)K(t-u) of the blocking at uu therefore still affects the occupancy at time tt, where K=1−GK=1-G is the service survival function. We show that, for every arrival process satisfying a functional strong law of large numbers, the fluid limit of this queue is a reflection with kernel KK of the fluid limit of the associated infinite-server queue. The reflection is defined by a one-sided reflection problem on (−∞,1](-\infty,1] in which the regulator is convolved with a memory kernel. Given a càdlàg input ρρ and a kernel KK with K(0)=1K(0)=1, we seek a pair (ρ^,η)(\hatρ,η) such that ρ^=ρ−∫0⋅K(⋅−u) dη(u)\hatρ=ρ-\int_0^\cdot K(\cdot-u)\,dη(u) takes values in (−∞,1](-\infty,1], ηη is nondecreasing, and ηη increases only when ρ^=1\hatρ=1. We refer to this problem as the Volterra-Skorokhod problem. We show existence and uniqueness of the solution and that the solution map is Lipschitz continuous in the uniform norm and nonanticipating. It therefore applies to stochastic inputs path by path. Unlike the classical Skorokhod map, this map is not monotone. For nonincreasing kernels, however, the regulator is the smallest admissible one and monotone in the input. In the queue, this orders the cumulative fluid losses under ordered arrival rates. Furthermore, since arrivals enter the queue through the reflection kernel, the regulator is a thinning of the arrivals.

Evidence graph

No public relationships recorded yet.

Integrity note: This page is a factual metadata record created by deterministic ingestion. It is not a claim that the work moves a mathematical frontier or has been independently verified.

Volterra-Skorokhod Reflection and Fluid Limits of Loss Queues — Mathematical Frontier Network