Optimized Framework for Composite Cloud Service Selection: A Computational Intelligence-Driven Approach

Abstract

Over the past decade, as demand for cloud services has surged, the strategic selection of these services has become increasingly crucial. The growing complexity within the cloud industry underscores the urgent need for a robust model for choosing cloud services effectively. Users often struggle to make informed decisions due to the dynamic nature and varying quality of available cloud services. In response, this paper introduces a novel decision-making approach aimed at optimizing the selection process by identifying the most suitable combination of cloud services. The focus is on integrating these services into a cohesive ensemble to better fulfill user requirements. In contrast to existing methodologies, our approach evaluates cloud services on a continuous scale, taking into account critical tasks such as workload balancing, storage management, and network resource handling. We propose a model for selecting optimal composite cloud services, which includes real-time optimization and addresses the consideration of null values for Quality of Service (QoS)-based attributes (e.g., response time, cost, availability, and reliability) in the dataset—a factor overlooked by current literature. The proposed algorithm is inspired by computational intelligence and driven by an evolutionary algorithm-based approach that undergoes evaluation across multiple datasets. The results illustrate its superiority, showcasing its ability to outperform existing optimization-based methods in terms of execution time.