分级存储中的块级数据迁移

Xiaonan Zhao, Zhanhuai Li, Xiao Zhang, Leijie Zeng
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引用次数: 12

摘要

管理数据增长仍然是IT面临的最大挑战。分层存储作为信息生命周期管理(ILM)的一种坚实的方式,有助于降低数据中心的成本,提高数据中心的存储效率。然而,数据迁移是实现分级存储管理的主要挑战,传统的数据迁移方案主要依靠管理员的经验,根据存储容量的使用情况和数据访问频率进行手动迁移,难以解决这一问题。本文提出了一种基于块级数据评估和全自动化过程的双向迁移策略(基于反馈的双阈值迁移,MDTF)。MDTF旨在通过双阈值实现存储QoS和迁移成本的平衡,缩小块级数据对象的迁移范围,分析表明,与传统的块级数据迁移策略相比,MDTF是一种高效的块级数据迁移策略。
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Block-Level Data Migration in Tiered Storage System
Managing data growth continues to be the biggest challenge for IT. And tiered storage, a solid way to Information Lifecycle Management (ILM), is very helpful to reduce the cost and improve the storage efficiency in data center. However, data migration is the primary challenge to implement tiered storage management, and it's hard to be resolved by traditional data migration solutions, which are mainly relying on experience of administrators and manual migration according to storage capacity usage and data access frequency. This paper proposes a novel bi-directional migration policy (migration with double thresholds based on feedback, MDTF), based on block-level data valuation and fully automation process. MDTF aims to achieve balance between storage QoS and migration costs by double thresholds, to narrow the migration scope of block-level data objects, and the analysis shows that MDTF is an efficient block-level data migration policy comparing with traditional migration policies.
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