Demonstration of effective UCB-based routing in skill-based queues on real-world data

TL;DR

This paper demonstrates the practical effectiveness of a UCB-based reinforcement learning algorithm for skill-based queue routing in real-world data centers, showing it adapts well, outperforms benchmarks, and can optimize multiple objectives.

Contribution

It introduces a practical implementation of a UCB-based routing algorithm with new heuristics and multi-objective optimization capabilities for real-world skill-based queues.

Findings

The algorithm learns and adapts efficiently to changing environments.

It outperforms static benchmark policies in real-world data.

The approach can incorporate multiple objectives like fairness and delay reduction.

Abstract

This paper is about optimally controlling skill-based queueing systems such as data centers, cloud computing networks, and service systems. By means of a case study using a real-world data set, we investigate the practical implementation of a recently developed reinforcement learning algorithm for optimal customer routing. Our experiments show that the algorithm efficiently learns and adapts to changing environments and outperforms static benchmark policies, indicating its potential for live implementation. We also augment the real-world applicability of this algorithm by introducing a new heuristic routing rule to reduce delays. Moreover, we show that the algorithm can optimize for multiple objectives: next to payoff maximization, secondary objectives such as server load fairness and customer waiting time reduction can be incorporated. Tuning parameters are used for balancing inherent performance trade--offs. Lastly, we investigate the sensitivity to estimation errors and parameter tuning, providing valuable insights for implementing adaptive routing algorithms in complex real-world queueing systems.