An integrated decision model for cloud vendor selection using probabilistic linguistic information and unknown weights. (September 2022)

• Record Type: Journal Article
• Authors: Krishankumar, R.; Supraja Nimmagadda, S.; Mishra, Arunodaya R.; Pamucar, Dragan; Ravichandran, K.S.; Gandomi, Amir H.
• Is Part Of: Engineering applications of artificial intelligence. Volume 114(2022)
• Publication Date: 2022-09
• Subjects: Cloud vendor selection -- Decision model -- Deviation method -- Mathematical model -- Probabilistic linguistic information; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.105114
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Because of the high competition among IT sectors, companies are planning to migrate to the cloud for effective growth and development. Driven by the importance of the cloud, new cloud vendors emerge each day to satisfy the demand of IT sectors. As a result, selection of an apt cloud vendor is critical. To this end, researchers have proposed different decision models, but these models do not effectively capture uncertainty during the decision-making process. To handle this issue, probabilistic linguistic information (PLI) is adopted in this paper, which associates occurrence probability to each term. Furthermore, weights of criteria are systematically determined using a deviation method, and cloud vendors are prioritized using a mathematical model under the PLI context. These methods are integrated to form the decision model, validated for its applicability using real case data from Cloud Armor. Finally, the advantages and weaknesses of the model are analyzed by using sensitivity analysis and comparison with extant models.