# Big jobs arrive early: From critical queues to random graphs

## TL;DR

This paper models a finite-pool queue with customer arrivals influenced by service requirements, revealing its connection to random graphs and deriving a diffusion limit for the queue length as the pool size grows.

## Contribution

It introduces a new queue model interpolating between known cases and characterizes its asymptotic behavior, linking queue dynamics to random graph structures.

## Key findings

- Queue length converges to a diffusion process with quadratic drift.
- Identifies the initial conditions needed for prolonged activity.
- Shows the emergence of a critically connected random forest from the queue process.

## Abstract

We consider a queue to which only a finite pool of $n$ customers can arrive, at times depending on their service requirement. A customer with stochastic service requirement $S$ arrives to the queue after an exponentially distributed time with mean $S^{-\alpha}$ for some $\alpha\in[0,1]$; so larger service requirements trigger customers to join earlier. This finite-pool queue interpolates between two previously studied cases: $\alpha = 0$ gives the so-called $\Delta_{(i)}/G/1$ queue and $\alpha = 1$ is closely related to the exploration process for inhomogeneous random graphs. We consider the asymptotic regime in which the pool size $n$ grows to infinity and establish that the scaled queue-length process converges to a diffusion process with a negative quadratic drift. We leverage this asymptotic result to characterize the head start that is needed to create a long period of activity. We also describe how this first busy period of the queue gives rise to a critically connected random forest.