Learning-Augmented Online Packet Scheduling with Deadlines
Ya-Chun Liang, Clifford Stein, Hao-Ting Wei

TL;DR
This paper introduces a learning-augmented online packet scheduling algorithm with deadlines that adapts to prediction accuracy, improving performance when predictions are accurate while maintaining robustness.
Contribution
It presents a novel framework for online packet scheduling with deadlines that leverages predictions to enhance competitive ratios, balancing accuracy and robustness.
Findings
Improves competitive ratio with accurate predictions
Maintains bounded competitive ratio regardless of prediction error
Provides a new algorithmic framework for learning-augmented scheduling
Abstract
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.
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Taxonomy
TopicsOptimization and Search Problems · Advanced Wireless Network Optimization · Caching and Content Delivery
