利用机器学习技术实现负载感知存储平台

Wubin Li, F. F. Moghaddam, P. Heidari, Y. Lemieux, Abdelouahed Gherbi
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引用次数: 1

摘要

在本文中,我们提出了工作负载感知存储平台的概念验证。该POC演示了构建机器学习技术支持的存储管理中间件的可行性。中间件能够为后端提供存储工作负载的最优分配,以及此后持续的动态优化。实验表明,该中间件能够在对工作负载影响最小的情况下,高效、动态地调整存储后端以满足SLA需求。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Facilitating Workload Aware Storage Platform by Using Machine Learning Technics
In this paper, we present our proof-of-concept of a workload aware storage platform. The POC demonstrates the feasibility of building a machine learning technics facilitated middleware for storage management. The middleware is capable of providing optimal assignments of storage workloads to backends as well as continuously on-the-fly optimization thereafter. Experiment indicates that the proposed middleware can efficiently and dynamically adapt the storage backend to satisfy the SLA requirements with minimum impact on the workloads.
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