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Cloud computing is the next generation of computer architectures and is the emerging field. In fact, it is an internet-enabled combination of computer resources. Amazon, Google, Sales and Microsoft are significant business and individual cloud computing service providers. The infrastructure, platform, and software are some examples of shared resources which may be customized. Consumers wishing to use cloud services should receive the services over the network. Cloud Service customers can buy services or utilize the services provided in the cloud free of charge. Because of the advantages and benefits of cloud use, many enterprises have started using the different services they offer. However, the work of the cloud user is difficult owing to the emergence of numerous Cloud providers on the market. The selection of the best CSP player is a challenge for cloud users such as cloud broker or enterprises due to similar features at different cost and performance. As a result, a framework is required to help cloud users in selecting and ranking CSPs in an efficient and accurate manner. In this context, standard QoS measurements for cloud services must be identified, as well as a ranking mechanism to evaluate CSPs based on QoS. To define the standard QoS measurements for services supplied by CSPs, the world's major worldwide organizations founded the Cloud Services Measurements Initiative Consortium (CSMIC) (SMI framework 2019). Majority of the existing works have focused on the quantitative SMI attributes alone (Kumar, 2018). As a result, a Multicriteria Decision Making technique may be useful for dealing with user or client needs and evaluating the cloud provider's services based on their capabilities. This study proposes a TOPSIS-based method for determining the best cloud service among a set of similar options, which is based on the cloud's qualitative Quality of Service characteristics. Different MCDM-based ranking methods, such as AHP (Garg et al, 2013), Fuzzy AHP with Delphi (Liu et al, 2016), and Fuzzy ELECTRE (Elimination and Option Expressing Reality)–Fuzzy TOPSIS (Lee, 2016), have been published recently. Tiwari et al (2021) presented an MCDM-based cloud service selection framework for selecting the optimal service provider based on QoS needs. The TOPSIS-based cloud service selection algorithms suffer from a rank reversal problem, since it ranks optimum service providers as non-optimal when a service provider is added or removed, deceiving the cloud customer. As a result, a revolutionary TOPSIS (RE-TOPSIS)-based framework has been suggested to rank cloud service providers based on the QoS they give and the cloud users' priority for each QoS. The suggested methodology is resilient for solving the rank reversal problem, and its efficacy has been shown in a case study using a real dataset. Kumar et al (2018) used the AHP weighting approach with the TOPSIS method to create a methodology for finding the best cloud service. The authors used AHP to build the architecture for the cloud service selection process and compute the criterion weights using pairwise comparison. The authors then used the TOPSIS technique to determine the cloud service's final rating based on overall performance. When compared to existing MCDM approaches, a real-time cloud case study confirmed the effectiveness of their suggested methodology. But in the case of above mentioned works (Tiwari et al,2021; Kumar et al, 2018), the authors did not address the inherent nature of fuzziness in the cloud service provider data set. So an appropriate MCDM approach that can handle vagueness or impreciseness in the dataset is needed for ranking the CSPs based on varied user requirements.