Demonstration of effective UCB-based routing in skill-based queues on real-world data
This work addresses routing efficiency in real-world systems like data centers, but it is incremental as it builds on existing methods with new heuristics and multi-objective tuning.
The paper tackled optimal routing in skill-based queueing systems using a reinforcement learning algorithm on real-world data, showing it outperforms static benchmarks and adapts to changing environments.
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.