Learning-Augmented Online Caching: New Upper Bounds
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866916865692925952 |
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| author | Skachkov, Daniel Ponomaryov, Denis Dorn, Yuri Demin, Alexander |
| author_facet | Skachkov, Daniel Ponomaryov, Denis Dorn, Yuri Demin, Alexander |
| contents | We address the problem of learning-augmented online caching in the scenario when each request is accompanied by a prediction of the next occurrence of the requested page. We improve currently known bounds on the competitive ratio of the BlindOracle algorithm, which evicts a page predicted to be requested last. We also prove a lower bound on the competitive ratio of any randomized algorithm and show that a combination of the BlindOracle with the Marker algorithm achieves a competitive ratio that is optimal up to some constant. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_01760 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning-Augmented Online Caching: New Upper Bounds Skachkov, Daniel Ponomaryov, Denis Dorn, Yuri Demin, Alexander Databases We address the problem of learning-augmented online caching in the scenario when each request is accompanied by a prediction of the next occurrence of the requested page. We improve currently known bounds on the competitive ratio of the BlindOracle algorithm, which evicts a page predicted to be requested last. We also prove a lower bound on the competitive ratio of any randomized algorithm and show that a combination of the BlindOracle with the Marker algorithm achieves a competitive ratio that is optimal up to some constant. |
| title | Learning-Augmented Online Caching: New Upper Bounds |
| topic | Databases |
| url | https://arxiv.org/abs/2410.01760 |