  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.
