Improving Cloud Resource Scaling A Comparative Analysis of Automatic Scaling Techniques Using the WASPAS Methodology

Cloud computing has transformed resource management by enabling scalable, on-demand services. Effective cloud management is critical to balancing performance, cost, and resource utilization. This study examines various cloud scaling techniques, including rule-based, predictive, container-based, serverless, and hybrid scaling, and evaluates their effectiveness in optimizing cloud resources while ensuring flexibility and reliability. This research provides a comparative analysis of cloud resource scaling strategies, which helps organizations select the most appropriate approach. By evaluating different scaling techniques, this study improves understanding of efficient cloud resource allocation, reduces costs, and ensures performance optimization. It contributes to cloud computing research by addressing key challenges in resource management. The alternative: Rule-Based Auto-Scaling, Predictive Auto-Scaling with AI/ML, Container-Based Scaling, Serverless Computing, Hybrid Scaling. The evaluation criteria consist of Scalability Efficiency, Resource Utilization, Over-Provisioning Risk, Complexity of Implementation. According to the results, Serverless Computing was ranked highest, while Hybrid Scaling was ranked lowest. Based on the Weighted Aggregated Sum Product Assessment System (WASPAS), serverless computing offers the highest value for cloud management for advanced resource scalability.