Multi-Objective Data Placement for Workflow Management in Cloud Infrastructure Using NSGA-II

Xiaolong Xu, Shucun Fu, Weimin Li*, Fei Dai, Honghao Gao, Victor Chang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

31 Citations (Scopus)


The cloud computing paradigm provides massive storage and rich computing resources for workflow deployment and implementation. Nevertheless, workflow applications (e.g., meteorological prediction and financial analysis) are usually data intensive, and substantial data resources with privacy information tend to be accessed during the workflow implementation. Therefore, it remains challenging to design a data placement method for seeking tradeoffs among multiple performance metrics, i.e., resource usage, data acquisition time, and energy cost, while avoiding privacy conflicts of information-overlapping datasets for workflow implementation of the cloud infrastructure. To address this challenge, a multi-objective data placement method for workflow management in the cloud infrastructure with privacy protection is proposed in this paper. Technically, the BCube topology is adopted to establish the resource model in the cloud infrastructure, and the potential privacy conflicts of datasets required for workflow implementation are analyzed. Then, a non-dominated sorting genetic algorithm II is leveraged to promote the resource usage, reduce the data acquisition time, and optimize the energy cost of the cloud infrastructure, while achieving the privacy protection for data placement. Finally, experimental evaluations demonstrate that the performance of the cloud infrastructure is optimized for workflow management.

Original languageEnglish
Article number9122026
Pages (from-to)605-615
Number of pages11
JournalIEEE Transactions on Emerging Topics in Computational Intelligence
Issue number5
Publication statusPublished - Oct 2020


  • Cloud computing
  • data placement
  • privacy preservation
  • workflow


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