BOSS: An Efficient Data Distribution Strategy for Object Storage Systems With Hybrid Devices
Hybrid object storage systems provide opportunities to achieve high performance and energy efficiency with low cost for enterprise data centers. Existing object storage systems, however, distribute data objects in the system without considering the heterogeneity of the underlying devices and the asy...
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Veröffentlicht in: | IEEE access 2017-01, Vol.5, p.23979-23993 |
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Sprache: | eng |
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Zusammenfassung: | Hybrid object storage systems provide opportunities to achieve high performance and energy efficiency with low cost for enterprise data centers. Existing object storage systems, however, distribute data objects in the system without considering the heterogeneity of the underlying devices and the asymmetric data access patterns. Therefore, the system performance and energy efficiency may degrade as data are placed on improper storage devices. For example, energy-efficient high-density archive hard disk drives (archive HDDs) are significantly slower than normal HDDs and solid state disks (SSDs), which mean that the archive HDDs are not appropriate for storing frequently accessed objects. Besides, flash-based SSDs have limited write endurance, which makes SSDs vulnerable for storing write-intensive objects. In this paper, we analyze various real enterprise workloads and find that read and write requests are not uniformly distributed to data objects. Based on the observations, we propose a novel strategy, biased object storage strategy (BOSS), to reduce writes to SSDs and improve system performance for hybrid object storage systems. Different from conventional uniform and fixed data distribution strategies, the BOSS can distribute and migrate data objects to various types of devices dynamically, according to the data access patterns collected online. The experimental results show that the BOSS can reduce 64% of writes on SSDs and improve system performance by 29.51% on average, while maintaining a high level of load balance. |
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ISSN: | 2169-3536 2169-3536 |
DOI: | 10.1109/ACCESS.2017.2744259 |