Author
Xu, X., Mo, R., Yin, X., Khosravi, M. R., Hosseinabadi, Fahimeh Aghaei, Chang, V., Li, G.
Publication Date
2021-08
Publication Place
-
IEEE
Subject
Cyber-physical cloud systems (CPCSs), Machine learning (ML), Nondominated sorting differential evolution (NSDE), Privacy-aware deployment
Type
Periodical
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
1551-3203
Record ID
15b7ccba-9b9c-48f8-b431-0802b4002500
Date
2021-08
Notes
Financial and Science Technology Plan Project of Xinjiang Production and Construction Corps ; National Natural Science Foundation of China (NSFC) ; Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) fund
Sample Text
The cyber-physical cloud systems (CPCSs) release powerful capability in provisioning the complicated industrial services. Due to the advances of machine learning (ML) in attack detection, a wide range of ML applications are involved in industrial CPCSs. However, how to ensure the implementation efficiency of these applications, and meanwhile avoid the privacy disclosure of the datasets due to data acquisition by different operators, remain challenging for the design of the CPCSs. To fill this gap, in this article a privacy-aware deployment method (PDM), named PDM, is devised for hosting the ML applications in the industrial CPCSs. In PDM, the ML applications are partitioned as multiple computing tasks with certain execution order, like workflows. Specifically, the deployment problem is formulated as a multiobjective problem for improving the implementation performance and resource utility. Then, the most balanced and optimal strategy is selected by leveraging an improved differential evolution technique. Finally, through comprehensive experiments and comparison analysis, PDM is fully evaluated.
DOI
10.1109/TII.2020.3031440
Cilt
17
