WNN-EDAS: A Wavelet Neural Network Based Multi-criteria Decision-Making Approach for Cloud Service Selection

Abstract

The omnipresence of the cloud-based applications and the exponential growth of the cloud services at different dimensions make the selection of user requirement compliant and trustworthy cloud service provider, a challenging task. Multi-Criteria Decision-Making (MCDM) approaches have their significance in solving cloud service selection problem since they evaluate the alternatives (cloud service providers) based on the intrinsic relationships among the criteria (QoS parameters). However, the assignment of appropriate weights to the criteria has a high impact on the accuracy of the service ranking and the performance of the MCDM methods. Hence, this paper presents a Wavelet Neural Network—Evaluation based on Distance from Average Solution (WNN-EDAS), a novel MCDM approach for the identification of suitable and trustworthy cloud service providers. WNN-EDAS employs WNN to calculate appropriate weights for each criterion and EDAS to rank the cloud service providers. The experiments were carried out on Cloud Armor, a real-world trust feedback dataset to demonstrate the accuracy, robustness, and feasibility of WNN-EDAS over the state-of-the-art MCDM approaches in terms of sensitivity analysis and rank reversal.