Dynamic Framework for Optimized Cloud Service Selection Using Adaptive Weighting and Enhanced TOPSIS

As cloud services become increasingly integral to business and technology landscapes, effective selection and composition of these services are paramount. This paper introduces a novel framework that significantly enhances the cloud service selection process by incorporating an advanced Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) algorithm. The proposed framework uniquely adapts to real-time user preferences and market trends through a dynamic weighting system, thereby refining decision-making accuracy. Comparative scenario-based testing shows an 88% improvement in decision accuracy over existing methods. Additionally, the modified TOPSIS algorithm achieves execution time reductions between 12
^%
and 45%, outperforming standard benchmarks. This framework is further distinguished by a dynamic weighting mechanism tailored to user feedback, which has resulted in high user satisfaction, averaging 4.2 out of 5. Comprehensive evaluation criteria within the framework ensure a thorough assessment of cloud service suitability, establishing a robust, adaptive, and user-centric approach to navigating the complex cloud service landscape.