Abstract
With the tremendous growth of Cloud Vendors, Cloud vendor (CV) prioritization is a complex decision-making problem. Previous studies on CV selection use functional and non-functional attributes, but do not have an apt structure for managing uncertainty in preferences. Motivated by this challenge, in this paper, a scientific framework for prioritization of CVs is proposed, which will help organizations to make decisions on service usage. Probabilistic linguistic term set (PLTS) is adopted as a structure for preference information, which manages uncertainty better by allowing partial information ignorance. Decision makers’ (DMs) relative importance is calculated using the programming model, by properly gaining the advantage of the partial knowledge and attributes, the weights are calculated using the extended statistical variance (SV) method. Further, DMs preferences are aggregated using a hybrid operator, and CVs are prioritized, using extended COPRAS method under the PLTS context. Finally, a case study on CV prioritization is provided for validating the scientific framework and the results are compared with other methods for understanding the strength and weakness of the proposal.
Highlights
Cloud computing is a powerful internet-based concept, that provides services to customers, based on their demand
The procedure for calculating weights when the information about attributes is completely unknown is given below: Step 1: Form a weight calculation matrix with Probabilistic linguistic term set (PLTS) information of order l × n where l denotes the number of Decision makers’ (DMs) and n denotes the number of attributes
This paper proposes a new decision framework under the PLTS context for the rational
Summary
Cloud computing is a powerful internet-based concept, that provides services to customers, based on their demand. The weight estimation methods, discussed in this category, do not capture the hesitation properly, and the aggregation of preferences ignores the calculation of decision maker’s weight and interrelationship among attributes. Prioritization of CVs by considering the nature of attributes and from different angles is lacking Motivated by these challenges and to address the same, some contributions are made: Probabilistic linguistic term set (PLTS) [21] is used as the data structure for preference elicitation, which manages uncertainty by associating occurring probability values for each linguistic term.
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