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Security and Communication Networks
Volume 2017, Article ID 2376484, 15 pages
Research Article

Data Placement for Privacy-Aware Applications over Big Data in Hybrid Clouds

1School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China
2Jiangsu Engineering Centre of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing, China
3State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China
4Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA
5School of Information and Control, Nanjing University of Information Science and Technology, Nanjing, China

Correspondence should be addressed to Wanchun Dou; nc.ude.ujn@cwuod

Received 17 August 2017; Accepted 24 September 2017; Published 8 November 2017

Academic Editor: Md Z. A. Bhuiyan

Copyright © 2017 Xiaolong Xu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Nowadays, a large number of groups choose to deploy their applications to cloud platforms, especially for the big data era. Currently, the hybrid cloud is one of the most popular computing paradigms for holding the privacy-aware applications driven by the requirements of privacy protection and cost saving. However, it is still a challenge to realize data placement considering both the energy consumption in private cloud and the cost for renting the public cloud services. In view of this challenge, a cost and energy aware data placement method, named CEDP, for privacy-aware applications over big data in hybrid cloud is proposed. Technically, formalized analysis of cost, access time, and energy consumption is conducted in the hybrid cloud environment. Then a corresponding data placement method is designed to accomplish the cost saving for renting the public cloud services and energy savings for task execution within the private cloud platforms. Experimental evaluations validate the efficiency and effectiveness of our proposed method.