2023/05/11 by Clifford Stein, Liang, Ya-Chun, Stein, Clifford +2
Computer Science · Engineering · #Advanced Wireless Network Optimization #Caching and Content Delivery #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Networking and Internet Architecture (cs.NI) #Optimization and Search Problems
paper · pdf · doi:10.48550/arxiv.2305.07164
openalex publication_date 2023/05/11 · openalex created_date 2023/05/17 · openalex updated_date 2026/07/28
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.