A Novel Framework for Cloud Service Selection Using Hybrid Principal Component Analysis (PCA) and Multicriteria Decision Making (MCDM) Methods

Abstract

The importance of quality of service (QoS) in categorizing cloud services has been increasing as the quantity of services offered by the cloud continues to grow. Cloud Computing is an internet technology that allows customers to use computing resources and pay based on demand in real-time. Various cloud service providers now offer similar services at different prices and performance levels, making it crucial but difficult for consumers to choose the best cloud service. The current techniques for selecting a cloud service allow users to provide choices in a quantifiable way. However, because many QoS variables are connected and not independent, the combined weights approach does not consider correlations between QoS attributes and can generate unreliable results. Our solution to this challenge involves implementing a cloud service architecture that considers customers’ choices and selects the best cloud service based on QoS limitations. We propose a hybrid cloud service selection approach that integrates principal component analysis (PCA) with a combined weighting mechanism incorporating both objective and subjective criteria. This dual strategy offers two key advantages: (1) it achieves a more well-rounded weighted result through the combined weighting strategy, and (2) it employs PCA to eliminate redundancy by removing correlations among QoS criteria, thereby enhancing the robustness of the selection process. The effectiveness and reliability of the proposed approach have been validated through simulations using the real-world QWS dataset, demonstrating its feasibility and superior performance in identifying the best cloud services.