Learning-Augmented Online Packet Scheduling with Deadlines
Fuente:
arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866929253606490112 |
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| author | Liang, Ya-Chun Stein, Clifford Wei, Hao-Ting |
| author_facet | Liang, Ya-Chun Stein, Clifford Wei, Hao-Ting |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_07164 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Learning-Augmented Online Packet Scheduling with Deadlines Liang, Ya-Chun Stein, Clifford Wei, Hao-Ting Data Structures and Algorithms Machine Learning Networking and Internet Architecture 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. |
| title | Learning-Augmented Online Packet Scheduling with Deadlines |
| topic | Data Structures and Algorithms Machine Learning Networking and Internet Architecture |
| url | https://arxiv.org/abs/2305.07164 |