Integrated Online Learning and Adaptive Control in Queueing Systems with Uncertain Payoffs

Abstract

Many online service platforms have dedicated algorithms to match their available resources to incoming clients to maximize client satisfaction. One of the key challenges is to balance the generation of higher payoffs from existing clients and exploration of new clients’ unknown characteristics while at the same time satisfy the resource capacity constraints. In “Integrated Online Learning and Adaptive Control in Queueing Systems with Uncertain Payoffs,” Hsu, Xu, Lin, and Bell show that traditional approaches such as maximizing instantaneous payoffs with current knowledge or using queue-length based controls guided by “shadow prices,” would lead to suboptimal long-term payoffs. Instead, they propose a novel utility-guided assignment algorithm that seamlessly integrates online learning and adaptive control to provide high system payoffs with performance guarantees. The theoretical performance bound also lends system design insights into the impact of uncertain client dynamics, payoff learning, and backlogged clients. They further develop a decentralized version of the algorithm, which is applicable to large systems and performs well even when the service rates are random.