Achieving Rapid Recovery in an Overload Control for Large-Scale Service Systems

TL;DR

This paper proposes an overload control mechanism for large-scale service systems that enables rapid recovery after overloads by using threshold-based sharing and a new six-dimensional fluid approximation to model system dynamics.

Contribution

It introduces a novel overload control strategy with lower thresholds for quick recovery and develops a six-dimensional fluid model for systems with time-varying arrivals.

Findings

The control algorithm effectively predicts system performance.

Proper parameter settings prevent oscillating congestion collapse.

Simulations confirm rapid recovery and system stability under the proposed method.

Abstract

We consider an automatic overload control for two large service systems modeled as multi-server queues, such as call centers. We assume that the two systems are designed to operate independently, but want to help each other respond to unexpected overloads. The proposed overload control automatically activates sharing (sending some customers from one system to the other) once a ratio of the queue lengths in the two systems crosses an activation threshold (with ratio and activation threshold parameters for each direction). To prevent harmful sharing, sharing is allowed in only one direction at any time. In this paper, we are primarily concerned with ensuring that the system recovers rapidly after the overload is over, either (i) because the two systems return to normal loading or (ii) because the direction of the overload suddenly shifts in the opposite direction. To achieve rapid recovery, we introduce lower thresholds for the queue ratios, below which one-way sharing is released. As a basis for studying the complex dynamics, we develop a new six-dimensional fluid approximation for a system with time-varying arrival rates, extending a previous fluid approximation involving a stochastic averaging principle. We conduct simulations to confirm that the new algorithm is effective for predicting the system performance and choosing effective control parameters. The simulation and the algorithm both show that the system can experience an inefficient nearly-periodic behavior, corresponding to an oscillating equilibrium (congestion collapse), if the sharing is strongly inefficient and the control parameters are set inappropriately.