Evidence-Based Cloud Vendor Assessment with Generalized Orthopair Fuzzy Information and Partial Weight Data

R. Krishankumar (), Dragan Pamucar () and K. S. Ravichandran ()
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R. Krishankumar: Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Department of Computer Science and Engineering
Dragan Pamucar: University of Defence, Department of Logistics, Military Academy
K. S. Ravichandran: Rajiv Gandhi National Institute of Youth Development

Chapter Chapter 8 in q-Rung Orthopair Fuzzy Sets, 2022, pp 197-217 from Springer

Abstract: Abstract As the information technology (IT) market booms globally, the urge for technological advancement grows. Cloud computing is a sophisticated technology that offers resources on demand. Due to the increase in computation, firms rely on cloud technology for resource management. Attracted by the abundant need, many cloud vendors evolve in the market, and selecting an apt vendor (CV) becomes complex due to the multiple service factors. Previous studies on CV selection incur lacunae viz., (i) uncertainty was not handled flexibly and (ii) personalized ranking was unavailable based on agent-driven data. Motivated by these lacunae and to glue the same, a scientific model is developed in this paper. A generalized orthopair fuzzy set is adopted for the flexible management of uncertainty and ease of preference sharing. Furthermore, a new mathematical model is formulated for factors’ significance assessment, and an evidence-based approximation approach is proposed for ranking CVs based on agent-driven data. Finally, a real case study of CV adoption by an academic institution is provided with a discussion on the merits and limitations of the model from theoretical and statistical perspectives.

Keywords: Cloud assessment; Evidence theory; Generalized fuzzy information; Mathematical model (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-19-1449-2_8

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DOI: 10.1007/978-981-19-1449-2_8

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