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<p>In recent decades, there has been an increased focus on evaluating cloud computing vendors in India. Cloud computing (CC) facilitates the transformation of on-premises information and communication technology (ICT) resources through the Internet. Users can access these services via cyberspace through a compatible subscription on-demand. The foundational service model in CC is Infrastructure as a Service (IaaS). The abundance of Infrastructure as a Service vendors (IaaSVs) in the cloud computing industry poses a challenge for users in selecting the most suitable IaaSV. This research aims to identify a competent IaaSV in the public domain based on predefined qualitative and quantitative criteria, framing it as a multi-criteria group decision-making (MCGDM) problem. The criteria data are gathered in a Pythagorean fuzzy environment. The Removal Effect of Criteria (MEREC) method is utilized to assess objective weights (OWs) of the criteria, while subjective weights (SWs) are computed using the Stepwise Weight Assessment Ratio Analysis (SWARA) method. A combination of OWs and SWs yields the final criteria weights. Incorporating decision makers’ psychological behaviour under risks, a prospect theory-based extended Pythagorean fuzzy TODIM (Interactive Multi-criteria Decision Making) method is employed, leveraging Pythagorean fuzzy Jensen-Shannon Song distance to rank five selected IaaSVs. The proposed model’s validity is demonstrated through information from the g2 online database and a comparative study. Lastly, a sensitivity analysis underscores the robustness of the devised method.</p>

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Extended Pythagorean fuzzy TODIM, MEREC and SWARA framework based IaaS vendor assessment

  • 期刊论文

  • 发表日期：

    2026年6月16日

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  • Tapas Kumar Paul,
  • Madhumangal Pal

摘要

In recent decades, there has been an increased focus on evaluating cloud computing vendors in India. Cloud computing (CC) facilitates the transformation of on-premises information and communication technology (ICT) resources through the Internet. Users can access these services via cyberspace through a compatible subscription on-demand. The foundational service model in CC is Infrastructure as a Service (IaaS). The abundance of Infrastructure as a Service vendors (IaaSVs) in the cloud computing industry poses a challenge for users in selecting the most suitable IaaSV. This research aims to identify a competent IaaSV in the public domain based on predefined qualitative and quantitative criteria, framing it as a multi-criteria group decision-making (MCGDM) problem. The criteria data are gathered in a Pythagorean fuzzy environment. The Removal Effect of Criteria (MEREC) method is utilized to assess objective weights (OWs) of the criteria, while subjective weights (SWs) are computed using the Stepwise Weight Assessment Ratio Analysis (SWARA) method. A combination of OWs and SWs yields the final criteria weights. Incorporating decision makers’ psychological behaviour under risks, a prospect theory-based extended Pythagorean fuzzy TODIM (Interactive Multi-criteria Decision Making) method is employed, leveraging Pythagorean fuzzy Jensen-Shannon Song distance to rank five selected IaaSVs. The proposed model’s validity is demonstrated through information from the g2 online database and a comparative study. Lastly, a sensitivity analysis underscores the robustness of the devised method.