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QuickDedup: Efficient VM deduplication in cloud computing environments

机译:快速:云计算环境中高效的VM重复数据删除

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Deduplication is one of the major storage optimisation techniques for Virtual Machines (VMs) in cloud environment. Usually, hashing of blocks helps in identifying duplicate data blocks. This paper proposes a novel deduplication approach, QuickDedup that reduces the overall deduplication time, metadata overhead and the number of hash computations, and subsequent comparisons for the VM disk images. In addition to minimising the deduplication related metadata, which is a necessary by-product useful in checking deduplication, QuickDedup, follows novel byte comparison scheme to prepare various block classes. This way, QuickDedup eliminates or minimises the need for hash calculation and subsequent comparisons. QuickDedup performs the calculation and comparisons of hashes within the respective categories only. QuickDedup saves the space required for hash storage during deduplication and makes deduplication of VM disk images much faster. We conducted a detailed evaluation of QuickDedup on various metrics with different kinds and sizes of VM images taken from publicly available datasets. The evaluation results show a substantial improvement of up to 96% in the overall deduplication time required to deduplicate VM images apart from significant savings in metadata and storage overhead.
机译:重复数据删除是云环境中虚拟机(VM)的主要存储优化技术之一。通常,块的散列有助于识别重复的数据块。本文提出了一种新型重复数据删除方法,快速尺寸减少了整体重复数据删除时间,元数据开销和散列计算的数量,以及随后的VM磁盘图像的比较。除了最小化重复数据删除相关元数据之外,它是一个必要的副产品,它是用于检查重复数据删除,快速数据删除,请遵循新的字节比较方案来准备各种块类。这样,快速加快消除或最小化对哈希计算的需要和随后的比较。 Quickdud仅在相应类别中执行哈希的计算和比较。快速保存重复数据删除期间哈希存储所需的空间,并使VM磁盘图像的重复数据删除更快。我们对不同种类的各种度量的快速评估,以及来自公开可用数据集的VM图像的各种度量。除了在元数据和存储开销的大量节省之外,重复数据删除时间,评估结果显示出高达96%的大幅提高高达96%。

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