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Trustworthiness prediction of cloud services based on selective neural network ensemble learning

文献类型：期刊论文

作者Mao, Chengying1; Lin, Rongru2; Towey, Dave3; Wang, Wenle4; Chen, Jifu1; He, Qiang5
刊名EXPERT SYSTEMS WITH APPLICATIONS
出版日期2021-04-15
卷号168页码:17
关键词Cloud services Trustworthiness prediction Selective ensemble learning Neural networks Particle swarm optimization (PSO)
ISSN号0957-4174
DOI10.1016/j.eswa.2020.114390
英文摘要Cloud services have become a popular and flexible solution for providing components to build service-based systems. A component's trustworthiness is a key measure that can guide service requesters when making a service selection decision. Prediction of this trustworthiness, based on the component's multi-faceted quality of service (QoS) attributes, is therefore an important problem to address. In this paper, selective ensemble learning is introduced to address the trust problem for cloud services: We use back-propagation neural networks (BPNNs) as the basic classifiers, with two swarm intelligence algorithms adapted to search for the optimal aggregation weights to create the ensemble: Basic particle swarm optimization (PSO) is used for decimal weights; and quantum discrete PSO (QPSO) is used for binary (0-1) weights. The optimized ensemble learning model, based on BPNNs, is then used to predict the trustworthiness of a given cloud service. Extensive experiments are performed on a well-known, public dataset to verify the effectiveness of the proposed trust prediction algorithms. The experimental results show that our algorithms are not only better than the basic BPNN method in prediction precision, but also outperform current state-of-the-art trust prediction algorithms. The proposed algorithms also exhibit a strong robustness.
资助项目National Natural Science Foundation of China[61762040] ; National Natural Science Foundation of China[61872167] ; Natural Science Foundation of Jiangxi Province, China[20162BCB23036] ; Natural Science Foundation of Jiangxi Province, China[20171ACB21031] ; Science Foundation of the Jiangxi Educational Committee, China[GJJ180276] ; Jiangxi Social Science Research Project, China[TQ-2015-202]
WOS研究方向Computer Science ; Engineering ; Operations Research & Management Science
语种英语
WOS记录号WOS:000614253700010
出版者PERGAMON-ELSEVIER SCIENCE LTD
资助机构National Natural Science Foundation of China ; Natural Science Foundation of Jiangxi Province, China ; Science Foundation of the Jiangxi Educational Committee, China ; Jiangxi Social Science Research Project, China
源URL[http://ir.idsse.ac.cn/handle/183446/8353]  
专题科学技术处
通讯作者Mao, Chengying
作者单位1.Jiangxi Univ Finance & Econ, Sch Software & IoT Engn, Nanchang 330013, Jiangxi, Peoples R China
2.Chinese Acad Sci, Inst Deep Sea Sci & Engn, Sanya 572000, Peoples R China
3.Univ Nottingham Ningbo China, Sch Comp Sci, Ningbo 315100, Peoples R China
4.Jiangxi Normal Univ, Sch Software, Nanchang 330022, Jiangxi, Peoples R China
5.Swinburne Univ Technol, Dept Comp Sci & Software Engn, Melbourne, Vic 3122, Australia
推荐引用方式
GB/T 7714		 Mao, Chengying,Lin, Rongru,Towey, Dave,et al. Trustworthiness prediction of cloud services based on selective neural network ensemble learning[J]. EXPERT SYSTEMS WITH APPLICATIONS,2021,168:17.
APA Mao, Chengying,Lin, Rongru,Towey, Dave,Wang, Wenle,Chen, Jifu,&He, Qiang.(2021).Trustworthiness prediction of cloud services based on selective neural network ensemble learning.EXPERT SYSTEMS WITH APPLICATIONS,168,17.
MLA Mao, Chengying,et al."Trustworthiness prediction of cloud services based on selective neural network ensemble learning".EXPERT SYSTEMS WITH APPLICATIONS 168(2021):17.

入库方式： OAI收割

来源：深海科学与工程研究所

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