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.
|Number of pages||10|
|Journal||IEEE Transactions on Industrial Informatics|
|Early online date||15 Oct 2020|
|Publication status||Published - 1 Aug 2021|
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This work was supported in part by the Financial and Science Technology Plan Project of Xinjiang Production and Construction Corps under Grant 2020DB005 in part by the National Natural Science Foundation of China under Grant 61702277, and in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) fund.
- Cyber-physical cloud systems (CPCSs)
- machine learning (ML)
- nondominated sorting differential evolution (NSDE)
- privacy-aware deployment