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Indexed by:Journal Papers

Date of Publication:2020-03-01

Journal:FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE

Included Journals:EI、SCIE

Volume:104

Page Number:74-91

ISSN No.:0167-739X

Key Words:Cloud Service Selection; Cloud computing; Fuzzy Linear Best Worst Method (FLBWM); Quality of Service (QoS); Multi-Criteria Decision Making (MCDM); Triangular Fuzzy Numbers (TFNs)

Abstract:Making a decision to shift from in-house to cloud computing is not an ordinary one. It involves cautious consideration of several key factors. The unavailability of precise information, ambiguous criteria and uncertainty of qualitative adjudication of decision makers further add to the problem. Enormous complexity and limitations of existing approaches make the service selection process extremely challenging and less trustworthy. To address such challenges, in this paper (1) we propose a novel framework to pave the way towards viable Cloud Service Selection as a Service (CSSaaS): (2) we implement the ranking/recommendation service of CSSaaS framework for viable cloud service ranking/selection under a fuzzy environment. For this purpose, we propose a novel Multicriteria Decision Making (MCDM) approach named Fuzzy Linear Best Worst Method (FLBWM). Contrary to crisp MCDM methods, FLBWM is robust, requires less data, produces authentic results and effectively handles imprecise/inexact information. To support the research, we present two illustrative applications including (1) selection of high-CPU compute optimized service and (2) selection of Infrastructure as a Service (laaS), using FLBWM. We perform a thorough comparative analysis to evaluate the performance and rank correlation of FLBWM with other decision-making methods. Moreover, we examine FLBWM in terms of sensitivity analysis, suitability for collaborative decision making, suitability under changes in alternatives and uncertainty management. The results favor the proposed approach. (C) 2019 Elsevier B.V. All rights reserved.