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A genetic algorithm based adaptive strategy for image backup of virtual machines

被引：0
|
作者：
Xu J.-W. [1 ,2 ,3 ]
Zhang W.-B. [1 ]
Wang T. [1 ]
Huang T. [1 ,2 ]
机构：
[1] Institute of Software, Chinese Academy of Sciences, Beijing
[2] State Key Laboratory of Computer Science, Beijing
[3] University of Chinese Academy of Sciences, Beijing
来源：
Jisuanji Xuebao/Chinese Journal of Computers | 2016年 / 39卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Cloud computing; Data deduplication; Genetic algorithm; Image backup; Virtual machine images;
D O I：
10.11897/SP.J.1016.2016.00351
中图分类号：
学科分类号：
摘要：
Virtual machine (VM) images are frequently backed up for service reliability in datacenters. However, the duplicated data of image backups take up a large amount of storage space. Thus, deduplication technologies are often used in backup operations to save storage space by removing duplicated data. Since backup operations with deduplication are resource intensive and time consuming, how to reduce the time of backup operations has become a key issue of datacenter management. Contemporary deduplication backup strategies can be summarized as deduplication after backup strategy, deduplication before backup strategy and deduplication during backup. As the strategies with different resource requirements are suitable for different scenarios, it is reasonable to combine them adaptively. This paper proposed an adaptive strategy for the deduplication backup of virtual machine images. We first profile the resource requirement of the deduplication backup operations with different strategies, and then use an object-oriented genetic algorithm to make a plan for minimizing the time of backup operations. Experimental results demonstrate that we can accurately estimate the deduplication backup time, and the algorithm saves about twenty percent deduplication backup time. © 2016, Science Press. All right reserved.
引用
收藏
页码：351 / 363
页数：12
相关论文
共 29 条
  • [1] Quinlan S., Dorward S., Venti: A new approach to archival storage, Proceedings of the Conference on File and Storage Technologies (FAST'02), pp. 89-101, (2002)
  • [2] Rhea S.C., Cox R., Pesterev A., Fast, inexpensive cntent-addressed storage in foundation, Proceedings of the USENIX Annual Technical Conference, pp. 143-156, (2008)
  • [3] Tolia N., Kozuch M., Satyanarayanan M., Et al., Opportunistic use of content addressable storage for distributed file systems, Proceedings of the General Track: 2003 USENIX Annual Technical Conference, pp. 127-140, (2003)
  • [4] Zhang W., Tang H., Jiang H., Et al., Multi-level selective deduplication for vm snapshots in cloud storage, Proceedings of the 5th IEEE International Conference on Cloud Computing (CLOUD'12), pp. 550-557, (2012)
  • [5] Bhagwat D., Eshghi K., Long D.D.E., Et al., Extreme binning: Scalable, parallel deduplication for chunk-based file backup, Proceedings of the 17th Annual Meeting of the IEEE/ACM International Symposium on Modelling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS'09), pp. 1-9, (2009)
  • [6] Fu Y., Jiang H., Xiao N., Et al., AA-Dedupe: An application-aware source deduplication approach for cloud backup services in the personal computing environment, Proceedings of the 2011 IEEE International Conference on Cluster Computing (CLUSTER), pp. 112-120, (2011)
  • [7] Won Y., Kim R., Ban J., Et al., Prun: Eliminating information redundancy for large scale data backup system, Proceedings of the 6th International Conference on Computational Sciences and Its Applications (ICCSA'08), pp. 139-144, (2008)
  • [8] Zhang W., Yang T., Narayanasamy G., Et al., Low-cost data deduplication for virtual machine backup in cloud storage, Proceedings of the 5th USENIX Conference on Hot Topics in Storage and File Systems (HotStorage'13), 13, (2013)
  • [9] Zhu B., Li K., Patterson R.H., Avoiding the disk bottleneck in the data domain deduplication file system, Proceedings of the 6th USENIX Conference on File and Storage Technologies (FAST'08), pp. 1-14, (2008)
  • [10] Kulkarni P., Douglis F., LaVoie J.D., Et al., Redundancy elimination within large collections of files, Proceedings of the General Track: 2004 USENIX Annual Technical Conference, pp. 59-72, (2004)
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