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Title:
Clustering-based acceleration for virtual machine image deduplication in the cloud environment
Author: Xu, JW ; Zhang, WB ; Zhang, ZY ; Wang, T ; Huang, T
Keyword: Cloud computing ; Virtualization ; VM image ; Deduplication
Source: JOURNAL OF SYSTEMS AND SOFTWARE
Issued Date: 2016
Volume: 121, Pages:144-156
Indexed Type: SCI
Department: Chinese Acad Sci, Inst Software, Beijing 100190, Peoples R China. State Key Lab Comp Sci, Beijing 100190, Peoples R China. Univ Chinese Acad Sci, Beijing 100190, Peoples R China.
Abstract: More and more virtual machine (VM) images are continuously created in datacenters. Duplicated data segments may exist in such VM images, and it leads to a waste of storage resource. As a result, VM image deduplication is a common daily activity in datacenters. Our previous work Crab is such a product and it is on duty regularly in our datacenter. The size of VM images is large and the amount of VM images is huge, and it is inefficient and impractical to load massive VM image fingerprints into memory for a fast comparison to recognize duplicated segments. To address this issue, we in this paper propose a clustering-based acceleration method. It uses an improved k-means clustering to find images having high chances to contain duplicated segments. With such a candidate selection phase, only limited VM image candidate fingerprints are loaded into memory. We empirically evaluate the effectiveness, robustness, and complexity of the proposed system. Experimental results show that it significantly reduces the performance interference to hosting virtual machine with an acceptable increase in disk space usage, compared with existing deduplication methods. (C) 2016 Elsevier Inc. All rights reserved.
English Abstract: More and more virtual machine (VM) images are continuously created in datacenters. Duplicated data segments may exist in such VM images, and it leads to a waste of storage resource. As a result, VM image deduplication is a common daily activity in datacenters. Our previous work Crab is such a product and it is on duty regularly in our datacenter. The size of VM images is large and the amount of VM images is huge, and it is inefficient and impractical to load massive VM image fingerprints into memory for a fast comparison to recognize duplicated segments. To address this issue, we in this paper propose a clustering-based acceleration method. It uses an improved k-means clustering to find images having high chances to contain duplicated segments. With such a candidate selection phase, only limited VM image candidate fingerprints are loaded into memory. We empirically evaluate the effectiveness, robustness, and complexity of the proposed system. Experimental results show that it significantly reduces the performance interference to hosting virtual machine with an acceptable increase in disk space usage, compared with existing deduplication methods. (C) 2016 Elsevier Inc. All rights reserved.
Language: 英语
WOS ID: WOS:000384864500011
Citation statistics:
Content Type: 期刊论文
URI: http://ir.iscas.ac.cn/handle/311060/17293
Appears in Collections:软件所图书馆_期刊论文

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Recommended Citation:
Xu, JW,Zhang, WB,Zhang, ZY,et al. Clustering-based acceleration for virtual machine image deduplication in the cloud environment[J]. JOURNAL OF SYSTEMS AND SOFTWARE,2016-01-01,121:144-156.
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