Optimizing cloud service provider selection with firefly-guided fuzzy decision support system for smart cities

Abstract

Businesses that want to benefit from cloud computing must choose a Cloud Service Provider (CSP). Cost, performance, Reliability, security, and SLAs must be evaluated during the decision process. CSP assessment is tough because of uncertainties and erroneous data. Fuzzy logic and the firefly optimization technique have been proposed in this paper to achieve optimal results based on diverse components. The proposed methodology uses consumer, service provider, and public reviews based on the three elements. These components' ratings can be used to analyze efficiency. Simple fuzzy logic is inferior to optimized fuzzy logic, according to experiments. The Firefly Optimized Fuzzy DSS is compared against non-optimized fuzzy decision-making systems and standard optimization methods. The results show that the proposed model is better for selecting the best CSP based on many parameters and managing assessment uncertainty. Fuzzy logic and optimization methods provide more nuanced and precise decision-making that accounts for subjective assessments and confusing facts. Businesses can make informed choices and ensure their CSP needs are satisfied with the approach. Finally, the Firefly Optimized Fuzzy Decision Support System offers a new perspective on cloud service provider selection by merging fuzzy logic with optimization. The system's ability to handle poor evaluations and ambiguity makes it ideal for CSP selection's complex decision-making process. This paper helps build decision support systems for choosing a cloud service provider and has substantial implications for firms seeking successful cloud computing solutions. This research work's conclusions have major implications for corporations and organizations searching for the finest cloud service providers. CSP-related real-world datasets are tested experimentally.