The modern network aims to prioritize critical traffic over non-critical traffic and effectively manage traffic flow. This necessitates proper buffer management to prevent the loss of crucial traffic while minimizing the impact on non-critical traffic. Therefore, the algorithm's objective is to control which packets to transmit and which to discard at each step. In this study, we initiate the learning-augmented online packet scheduling with deadlines and provide a novel algorithmic framework to cope with the prediction. We show that when the prediction error is small, our algorithm improves the competitive ratio while still maintaining a bounded competitive ratio, regardless of the prediction error.

翻译：现代网络旨在优先处理关键流量而非非关键流量，并有效管理流量流。这需要适当的缓冲区管理，以防止关键流量丢失，同时最小化对非关键流量的影响。因此，算法的目标是控制每一步中哪些数据包应被传输、哪些应被丢弃。在本研究中，我们首次提出学习增强型截止时间在线数据包调度问题，并设计了一个新颖的算法框架来应对预测。我们证明，当预测误差较小时，我们的算法能够提升竞争比，同时无论预测误差大小如何，仍能保持有界的竞争比。