Learning-Augmented Online Packet Scheduling with Deadlines. (arXiv:2305.07164v1 [cs.DS])

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 …

algorithm arxiv control flow impact loss management network scheduling traffic