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 publically 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.

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。本文提出了一种新颖的重复数据删除方法QuickDedup，该方法可减少总体重复数据删除时间，元数据开销和哈希计算次数，并减少VM磁盘映像的后续比较。除了最小化重复数据删除相关的元数据（这是检查重复数据删除的必要副产品）之外，QuickDedup还遵循新颖的字节比较方案来准备各种块类。这样，QuickDedup消除或最小化了哈希计算和后续比较的需求。QuickDedup仅在相应类别内执行哈希的计算和比较。QuickDedup节省了重复数据删除期间哈希存储所需的空间，并使VM磁盘映像的重复数据删除更快。我们使用从公开可用的数据集中获取的具有不同种类和大小的VM映像的各种指标对QuickDedup进行了详细的评估。评估结果表明，除了显着节省元数据和存储开销外，对VM映像进行重复数据删除所需的总体重复数据删除时间最多可提高96％。