Open Access

ARTICLE

QoS-Aware Cloud Service Optimization Algorithm in Cloud Manufacturing Environment

Wenlong Ma^1,2,*, 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:

Intelligent Automation & Soft Computing 2023, 37(2), 1499-1512. https://doi.org/10.32604/iasc.2023.030484

Received 27 March 2022; Accepted 24 June 2022; Issue published 21 June 2023

Abstract

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.

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Keywords

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