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Decision making framework for heterogeneous QoS information: an application to cloud service selection

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Abstract

In recent times, appropriate decision-making in challenging and critical situations has been very well supported by multicriteria decision-making (MCDM) methods. The technique for order of preference by similarity to ideal solution (TOPSIS) is the most widely used MCDM method for solving decision problems. However, it restricts decision-makers to use only one type of Quality of Service (QoS) information, and it suffers from the rank reversal problem. Restriction to only one type of QoS makes the decision problems more challenging, as it restricts the decision-makers freedom. Further, the rank reversal problem makes the decision result unreliable. To address these issues of TOPSIS, we have proposed a reliable rank reversal robust modular TOPSIS (RMo-TOPSIS). RMo-TOPSIS allows crisp, interval, fuzzy, intuitionistic and neutrosophic fuzzy QoS metrics. It does not suffer from the rank reversal problem. Cloud computing provides computing services on-demand basis without involving maintenance by its users. The availability of many cloud service providers and their services makes cloud service selection a challenging problem. To validate RMo-TOPSIS, we select the cloud service selection consisting of different types of QoS metrics as an application. Experiments on cloud service selection show consistency and accuracy in results obtained by RMo-TOPSIS and its robustness against rank reversal.

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Data Availability Statement

The authors confrm that the data supporting the findings of this study are available within the article.

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Acknowledgements

We are thankful to the TEQIP-III project of the Ministry of Human Resource Development, Government of India for financially supporting this study.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Madan Mohan Malaviya University of Technology, Gorakhpur, 273010, India

    Rohit Kumar Tiwari & Rakesh Kumar

  2. Department of Computer Science, BHU, Varanasi, India

    Gaurav Baranwal

  3. Cloud Computing and Distributed Systems Laboratory, School of Computing and Information Systems, University of Melbourne, Melbourne, Australia

    Rajkumar Buyya

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  1. Rohit Kumar Tiwari
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Tiwari, R.K., Kumar, R., Baranwal, G. et al. Decision making framework for heterogeneous QoS information: an application to cloud service selection. J Ambient Intell Human Comput 14, 2915–2934 (2023). https://doi.org/10.1007/s12652-023-04532-w

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