A Fluid Limit for an Overloaded X Model Via a Stochastic Averaging Principle

TL;DR

This paper establishes a fluid limit for an overloaded two-class, two-pool queueing system under the FQR-T control, using a stochastic averaging principle to handle the complex time-scale separation.

Contribution

It introduces a novel fluid limit proof for the X model with FQR-T control, leveraging a stochastic averaging principle to manage fast-time-scale processes.

Findings

Proves a heavy-traffic fluid limit for the overloaded X model.

Demonstrates the effectiveness of the stochastic averaging principle in this context.

Shows the system maintains a fixed queue ratio asymptotically.

Abstract

We prove a many-server heavy-traffic fluid limit for an overloaded Markovian queueing system having two customer classes and two service pools, known in the call-center literature as the X model. The system uses the fixed-queue-ratio-with-thresholds (FQR-T) control, which we proposed in a recent paper as a way for one service system to help another in face of an unexpected overload. Under FQR-T, customers are served by their own service pool until a threshold is exceeded. Then, one-way sharing is activated with customers from one class allowed to be served in both pools. After the control is activated, it aims to keep the two queues at a pre-specified fixed ratio. For large systems that fixed ratio is achieved approximately. For the fluid limit, or FWLLN, we consider a sequence of properly scaled X models in overload operating under FQR-T. Our proof of the FWLLN follows the compactness approach, i.e., we show that the sequence of scaled processes is tight, and then show that all converging subsequences have the specified limit. The characterization step is complicated because the queue-difference processes, which determine the customer-server assignments, remain stochastically bounded, and need to be considered without spatial scaling. Asymptotically, these queue-difference processes operate in a faster time scale than the fluid-scaled processes. In the limit, due to a separation of time scales, the driving processes converge to a time-dependent steady state (or local average) of a time-varying fast-time-scale process (FTSP). This averaging principle (AP) allows us to replace the driving processes with the long-run average behavior of the FTSP.