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.
|Original language||English (US)|
|Title of host publication||2022 IEEE International Conference on Networking, Architecture and Storage, NAS 2022 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|State||Published - 2022|
|Event||16th IEEE International Conference on Networking, Architecture and Storage, NAS 2022 - Philadelphia, United States|
Duration: Oct 3 2022 → Oct 4 2022
|Name||2022 IEEE International Conference on Networking, Architecture and Storage, NAS 2022 - Proceedings|
|Conference||16th IEEE International Conference on Networking, Architecture and Storage, NAS 2022|
|Period||10/3/22 → 10/4/22|
Bibliographical noteFunding Information:
VIII. ACKNOWLEDGEMENT This work was partially supported by NSF I/UCRC Center Research in Intelligent Storage and the following NSF awards 1439622, 1812537, 2204656, and 2204657.
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