Optimizing SSD Caches for Cloud Block Storage Systems Using Machine Learning Approaches
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866917904685989888 |
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| author | Cheng, Chiyu Zhou, Chang Zhao, Yang Cao, Jin |
| author_facet | Cheng, Chiyu Zhou, Chang Zhao, Yang Cao, Jin |
| contents | The growing demand for efficient cloud storage solutions has led to the widespread adoption of Solid-State Drives (SSDs) for caching in cloud block storage systems. The management of data writes to SSD caches plays a crucial role in improving overall system performance, reducing latency, and extending the lifespan of storage devices. A critical challenge arises from the large volume of write-only data, which significantly impacts the performance of SSD caches when handled inefficiently. Specifically, writes that have not been read for a certain period may introduce unnecessary write traffic to the SSD cache without offering substantial benefits for cache performance. This paper proposes a novel approach to mitigate this issue by leveraging machine learning techniques to dynamically optimize the write policy in cloud-based storage systems. The proposed method identifies write-only data and selectively filters it out in real-time, thereby minimizing the number of unnecessary write operations and improving the overall performance of the cache system. Experimental results demonstrate that the proposed machine learning-based policy significantly outperforms traditional approaches by reducing the number of harmful writes and optimizing cache utilization. This solution is particularly suitable for cloud environments with varying and unpredictable workloads, where traditional cache management strategies often fall short. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_14770 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optimizing SSD Caches for Cloud Block Storage Systems Using Machine Learning Approaches Cheng, Chiyu Zhou, Chang Zhao, Yang Cao, Jin Distributed, Parallel, and Cluster Computing Machine Learning Operating Systems The growing demand for efficient cloud storage solutions has led to the widespread adoption of Solid-State Drives (SSDs) for caching in cloud block storage systems. The management of data writes to SSD caches plays a crucial role in improving overall system performance, reducing latency, and extending the lifespan of storage devices. A critical challenge arises from the large volume of write-only data, which significantly impacts the performance of SSD caches when handled inefficiently. Specifically, writes that have not been read for a certain period may introduce unnecessary write traffic to the SSD cache without offering substantial benefits for cache performance. This paper proposes a novel approach to mitigate this issue by leveraging machine learning techniques to dynamically optimize the write policy in cloud-based storage systems. The proposed method identifies write-only data and selectively filters it out in real-time, thereby minimizing the number of unnecessary write operations and improving the overall performance of the cache system. Experimental results demonstrate that the proposed machine learning-based policy significantly outperforms traditional approaches by reducing the number of harmful writes and optimizing cache utilization. This solution is particularly suitable for cloud environments with varying and unpredictable workloads, where traditional cache management strategies often fall short. |
| title | Optimizing SSD Caches for Cloud Block Storage Systems Using Machine Learning Approaches |
| topic | Distributed, Parallel, and Cluster Computing Machine Learning Operating Systems |
| url | https://arxiv.org/abs/2501.14770 |