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QoS-Aware Cloud Service Optimization Algorithm in Cloud Manufacturing Environment

Wenlong Ma1,2,*, Youhong Xu1, Jianwei Zheng2, Sadaqat ur Rehman3

1 School of Information Engineering, Quzhou College of Technology, Quzhou, 324000, China
2 School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, 310023, China
3 Department of Natural and Computing Science, University of Aberdeen, Scotland, Aberdeen, AB243FX, UK

* Corresponding Author: Wenlong Ma. Email: email

Intelligent Automation & Soft Computing 2023, 37(2), 1499-1512.


In a cloud manufacturing environment with abundant functionally equivalent cloud services, users naturally desire the highest-quality service(s). Thus, a comprehensive measurement of quality of service (QoS) is needed. Optimizing the plethora of cloud services has thus become a top priority. Cloud service optimization is negatively affected by untrusted QoS data, which are inevitably provided by some users. To resolve these problems, this paper proposes a QoS-aware cloud service optimization model and establishes QoS-information awareness and quantification mechanisms. Untrusted data are assessed by an information correction method. The weights discovered by the variable precision Rough Set, which mined the evaluation indicators from historical data, providing a comprehensive performance ranking of service quality. The manufacturing cloud service optimization algorithm thus provides a quantitative reference for service selection. In experimental simulations, this method recommended the optimal services that met users’ needs, and effectively reduced the impact of dishonest users on the selection results.


Cite This Article

W. Ma, Y. Xu, J. Zheng and S. U. Rehman, "Qos-aware cloud service optimization algorithm in cloud manufacturing environment," Intelligent Automation & Soft Computing, vol. 37, no.2, pp. 1499–1512, 2023.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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