Machine Learning-based Adaptive Migration Algorithm for Hybrid Storage Systems
Machine Learning-based Adaptive Migration Algorithm for Hybrid Storage Systems
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DOI：
10.1109/nas55553.2022.9925545
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发表时间：
2022-10
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影响因子：
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通讯作者：
Milan M. Shetti;Bingzhe Li;D. Du
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文献类型：
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作者：
Milan M. Shetti;Bingzhe Li;D. Du
混合存储系统在大多数大型企业存储系统中普遍存在，因为它平衡了存储性能、存储容量和成本。此类系统的目标是满足高性能设备的大部分 I/O 请求，并将不常用的数据存储在低性能设备中。在实际的混合存储系统中，层之间的大量数据迁移可能会导致巨大的开销。因此，如何平衡迁移成本和潜在性能增益之间的权衡是混合存储系统中具有挑战性且关键的问题。在本文中，我们重点研究具有两类存储设备的混合存储系统的数据迁移问题。提出了一种基于机器学习的迁移算法，称为K-Means辅助支持向量机（K-SVM）迁移算法。该算法能够更精确地分类并在性能层和容量层之间高效地迁移数据。此外，该K-SVM迁移算法涉及K-Means聚类算法来动态选择合适的训练数据集，使得该算法可以显着减少迁移数据量。最后，真实的实现结果表明，基于ML的算法比其他算法减少了约40%的迁移数据量，并实现了70%的低延迟。
Hybrid storage systems are prevalent in most large-scale enterprise storage systems since they balance storage performance, storage capacity and cost. The goal of such systems is to serve the majority of the I/O requests from high-performance devices and store less frequently used data in low-performance devices. A large data migration volume between tiers can cause a huge overhead in practical hybrid storage systems. Therefore, how to balance the trade-off between the migration cost and potential performance gain is a challenging and critical issue in hybrid storage systems. In this paper, we focused on the data migration problem of hybrid storage systems with two classes of storage devices. A machine learning-based migration algorithm called K-Means assisted Support Vector Machine (K-SVM) migration algorithm is proposed. This algorithm is capable of more precisely classifying and efficiently migrating data between performance and capacity tiers. Moreover, this K-SVM migration algorithm involves a K-Means clustering algorithm to dynamically select a proper training dataset such that the proposed algorithm can significantly reduce the volume of migrating data. Finally, the real implementation results indicate that the ML-based algorithm reduces the migration data volume by about 40% and achieves 70% lower latency than other algorithms.