Adaptive Queue Scheduling in Polling Systems via Online Deep Learning

To optimize queue management in dynamic communication and processing environments, this paper proposes an AI-driven adaptive polling mechanism that makes real-time service decisions based on system states. Through extensive simulations under fluctuating and unbalanced traffic conditions, the proposed method is demonstrated to significantly reduce average waiting times at highload queues compared to traditional cyclic and skipped-empty policies, while maintaining low computational complexity