Analyzing Queueing Problems via Bandits With Linear Reward & Nonlinear Workload Fairness

Queueing models serve as important building blocks in many networking applications such as task scheduling in mobile edge computing nodes, traffic scheduling in networks, congestion control in Internet, etc. However, queueing theory often needs to make strong assumptions about the arrival process or service rewards at each queue. In addition, fairness in serving workload among all queues is of great importance in many applications. In this paper, we address how to optimize resource allocation among multiple queues with a fairness guarantee and without any a priori knowledge of these queues’ parameters. To characterize queues with unknown parameters and the fairness requirement, we formulate an online learning model with a varying and continuous action space, as well as a nonlinear utility objective. We design an online learning algorithm to tackle the problem. We prove that our algorithm has a regret upper bound of
$O(\sqrt{T}\log T)$
and our model has a regret lower bound of
$\Omega (\sqrt{T})$
, where
$T$
stands for the number of decision rounds. The asymptotic closeness of upper and lower bounds guarantees their near tightness and our algorithm's near optimality. We discuss our model's real-world applications in mobile edge computing, wireless networks, and crowdsourcing, and conduct simulations to validate our algorithm's effectiveness.