Towards using Reinforcement Learning for Scaling and Data Replication in Cloud Systems
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866910652095791104 |
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| author | Mokadem, Riad Arar, Fahem Zegour, Djamel Eddine |
| author_facet | Mokadem, Riad Arar, Fahem Zegour, Djamel Eddine |
| contents | Given its intuitive nature, many Cloud providers opt for threshold-based data replication to enable automatic resource scaling. However, setting thresholds effectively needs human intervention to calibrate thresholds for each metric and requires a deep knowledge of current workload trends, which can be challenging to achieve. Reinforcement learning is used in many areas related to the Cloud Computing, and it is a promising field to get automatic data replication strategies. In this work, we survey data replication strategies and data scaling based on reinforcement learning (RL). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_11862 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards using Reinforcement Learning for Scaling and Data Replication in Cloud Systems Mokadem, Riad Arar, Fahem Zegour, Djamel Eddine Distributed, Parallel, and Cluster Computing Artificial Intelligence Given its intuitive nature, many Cloud providers opt for threshold-based data replication to enable automatic resource scaling. However, setting thresholds effectively needs human intervention to calibrate thresholds for each metric and requires a deep knowledge of current workload trends, which can be challenging to achieve. Reinforcement learning is used in many areas related to the Cloud Computing, and it is a promising field to get automatic data replication strategies. In this work, we survey data replication strategies and data scaling based on reinforcement learning (RL). |
| title | Towards using Reinforcement Learning for Scaling and Data Replication in Cloud Systems |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence |
| url | https://arxiv.org/abs/2410.11862 |