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

Abstract

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.