A novel datacenter-oriented data placement strategy of scientific workflow in hybrid cloud

Li, Xue-Jun, Wu, Yang, Liu, Xiao, Cheng, Hui-Min, Zhu, Er-Zhou and Yang, Yun 2016, A novel datacenter-oriented data placement strategy of scientific workflow in hybrid cloud, Ruan jian xue bao/Journal of software, vol. 27, no. 7, pp. 1861-1875, doi: 10.13328/j.cnki.jos.004879.

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Title A novel datacenter-oriented data placement strategy of scientific workflow in hybrid cloud
Author(s) Li, Xue-Jun
Wu, Yang
Liu, XiaoORCID iD for Liu, Xiao orcid.org/0000-0001-8400-5754
Cheng, Hui-Min
Zhu, Er-Zhou
Yang, Yun
Journal name Ruan jian xue bao/Journal of software
Volume number 27
Issue number 7
Start page 1861
End page 1875
Total pages 14
Publisher Chinese Academy of Sciences/Institute of Software
Place of publication Beijing, China
Publication date 2016
ISSN 1000-9825
Keyword(s) Scientific workflow
Cloud computing
Hybrid cloud
Data placement
Transfer time
Summary Scientific workflow is a complicated data intensive application. How to achieve an effective data placement schema in hybrid cloud environment has become a crucial issue nowadays, especially with the new challenges brought by the security issues. Traditional data placement strategies usually adopt load balancing-based partition model to allocate datasets. Although these data placement schemas can have good performance in load balancing, their data transfer time may not be optimal. In contrast to traditional strategies, this paper focuses on the hybrid cloud environment and proposes a data dependency destruction-based partition model to achieve the minimal data dependency destruction partition. In addition, it presents a novel datacenter-oriented data placement strategy. This strategy allocates high dependency datasets to one datacenter according to the new partition model and thus significantly reduces data transfer time between datacenters. Experimental results show that the proposed strategy can effectively reduce data transfer time during workflow's execution.
Language chi
DOI 10.13328/j.cnki.jos.004879
Field of Research 080109 Pattern Recognition and Data Mining
080503 Networking and Communications
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, Institute of Software, the Chinese Academy of Sciences
Persistent URL http://hdl.handle.net/10536/DRO/DU:30085718

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