A Heavy Traffic Theory of Matching Queues

TL;DR

This paper develops a heavy traffic theory for matching queues, revealing phase transitions in queue behavior depending on control cost and scaling parameters, with implications for online matching platforms.

Contribution

It introduces a novel asymptotic regime analysis for matching queues, identifying three distinct regimes and their limiting distributions based on control cost and scaling parameters.

Findings

Delay-driven regime with asymmetrical Laplace distribution

Cost-driven regime with uniform or truncated exponential distribution

Hybrid regime with Gibbs distribution

Abstract

Motivated by emerging applications in online matching platforms and marketplaces, we study a matching queue. Customers and servers that arrive in a matching queue depart as soon as they are matched. While state-dependent control is an effective lever to regulate the throughput and delay, it often comes at a cost in practice for matching platforms. Optimizing this fundamental trade-off motivates the use of small amounts of control, so we study a matching queue in an asymptotic regime where the state-dependent control decreases to zero. Unlike the heavy traffic regime in classical queues, there are two different ways the control can be sent to zero, via a magnitude scaling parameter $\epsilon$ that goes to zero and a time scaling parameter $\tau$ that goes to infinity. Depending on the cost of control, we show that the rates of $\epsilon$ and $\tau$ that optimize the trade-off between delay and cost of control could correspond to three different regimes. As we traverse these regimes, we observe a phase transition in the limiting distribution of the matching queue. We show that a low cost of control corresponds to the regime $\epsilon \tau \rightarrow 0$ and we call it the delay-driven regime. The limiting behavior in this regime is an asymmetrical Laplace distribution. On the other hand, $\epsilon \tau \rightarrow \infty$ is the cost-driven regime corresponding to a high cost of control where the limiting behavior is either a uniform or a truncated exponential distribution. We christen the in-between regime of $\epsilon\tau \rightarrow (0, \infty)$ the hybrid regime where the limiting behavior is a Gibbs distribution. These results are obtained by novel generalizations of the transform method, where each regime requires new ideas. The hybrid regime employs inverse Fourier transforms while the other two regimes engineer multiple complex exponential test functions.