An Improved CoCoSo Method with a Maximum Variance Optimization Model for Cloud Service Provider Selection


  • Han Lai Chongqing Technology and Business University, China
  • Huchang Liao Business School, Sichuan University, China
  • Zhi Wen Business School, Sichuan University, China
  • Edmundas Kazimieras Zavadskas Vilnius Gediminas Technical University, Lithuania
  • Abdullah Al-Barakati Faculty of Computing and Information Technology, King Abdulaziz University, Saudi Arabia



Multi-criteria decision making, Cloud service providers selection, Combined compromise solution (CoCoSo), Maximum variance, Discrimination degree


With the rapid growth of available online cloud services and providers for customers, the selection of cloud service providers plays a crucial role in on-demand service selection on a subscription basis. Selecting a suitable cloud service provider requires a careful analysis and a reasonable ranking method. In this study, an improved combined compromise solution (CoCoSo) method is proposed to identify the ranking of cloud service providers. Based on the original CoCoSo method, we analyze the defects of the final aggregation operator in the original CoCoSo method which ignores the equal importance of the three subordinate compromise scores, and employ the operator of “Linear Sum Normalization” to normalize the three subordinate compromise scores so as to make the results reasonable. In addition, we introduce a maximum variance optimization model which can increase the discrimination degree of evaluation results and avoid inconsistent ordering. A numerical example of the trust evaluation of cloud service providers is given to demonstrate the applicability of the proposed method. Furthermore, we perform sensitivity analysis and comparative analysis to justify the accuracy of the decision outcomes derived by the proposed method. Besides, the results of discrimination test also indicate that the proposed method is more effective than the original CoCoSo method in identifying the subtle differences among alternatives.

Author Biographies

Han Lai, Chongqing Technology and Business University, China

Han Lai, PhD, is a lecturer at Chongqing Engineering Laboratory for Detection, Control and Integrated System, Chongqing Technology and Business University. His research interests include Decision-making Theory and Application ,Requirement Engineering, Cloud Computing, etc.

Huchang Liao, Business School, Sichuan University, China

Huchang Liao (M’13–SM’17) received his Ph.D. degree in Management Science and Engineering from the Shanghai Jiao Tong University, Shanghai, China, in 2015. He is currently a Research Fellow in the Business School at the Sichuan University, Chengdu, China. He has published 3 monographs, 1 chapter, and more than 190 peer-reviewed papers, many in high-quality international journals including European Journal of Operational Research, Omega, IEEE Transactions on Fuzzy Systems, IEEE Transaction on Cybernetics, Information Sciences, Information Fusion, etc.  He is the Editor-In-Chief, Associate Editor, Guest Editor or Editorial Board Member for 30 international journals. Prof Liao has received numerous honors and awards, including the Thousand Talents Plan for Young Professionals in Sichuan Province, the Candidate of Academic and Technical Leaders in Sichuan Province, the Outstanding scientific research achievement award in higher institutions (First class in Natural Science in 2017; Second class in Natural Science in 2019), the Outstanding scientific science research achievement award in Sichuan Province (Second class in Social Science in 2019), and the 2015 Endeavour Research Fellowship award granted by the Australia Government.


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