POD: Performance Oriented I/O Deduplication for Primary Storage Systems in the Cloud

Bo Mao, Hong Jiang, Suzhen Wu, Lei Tian
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引用次数: 40

Abstract

Recent studies have shown that moderate to high data redundancy clearly exists in primary storage systems in the Cloud. Our experimental studies reveal that data redundancy exhibits a much higher level of intensity on the I/O path than that on disks due to the relatively high temporal access locality associated with small I/O requests to redundant data. On the other hand, we also observe that directly applying data deduplication to primary storage systems in the Cloud will likely cause space contention in memory and data fragmentation on disks. Based on these observations, we propose a Performance-Oriented I/O Deduplication approach, called POD, rather than a capacity-oriented I/O deduplication approach, represented by iDedup, to improve the I/O performance of primary storage systems in the Cloud without sacrificing capacity savings of the latter. The salient feature of POD is its focus on not only the capacity-sensitive large writes and files, as in iDedup, but also the performance-sensitive while capacity-insensitive small writes and files. The experiments conducted on our lightweight prototype implementation of POD show that POD significantly outperforms iDedup in the I/O performance measure by up to 87.9% with an average of 58.8%. Moreover, our evaluation results also show that POD achieves comparable or better capacity savings than iDedup.
POD:面向云主存储系统的性能I/O重复数据删除
最近的研究表明,在云中的主存储系统中明显存在中度到高度的数据冗余。我们的实验研究表明,数据冗余在I/O路径上表现出比在磁盘上高得多的强度,因为与对冗余数据的小I/O请求相关的相对较高的时间访问局域性。另一方面,我们还观察到,直接对云中的主存储系统应用重复数据删除可能会导致内存中的空间争用和磁盘上的数据碎片。基于这些观察,我们提出了一种面向性能的I/O重复数据删除方法(称为POD),而不是以iDedup为代表的面向容量的I/O重复数据删除方法(以iDedup为代表),以提高云中的主存储系统的I/O性能,而不会牺牲后者节省的容量。POD的显著特性是,它不仅关注容量敏感的大写操作和文件(如iDedup),还关注性能敏感而容量不敏感的小写操作和文件。在我们的POD轻量级原型实现上进行的实验表明,POD在I/O性能测量方面明显优于iDedup,最高可达87.9%,平均为58.8%。此外,我们的评估结果还表明,POD实现了与iDedup相当甚至更好的容量节省。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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