A Novel Framework for Cloud Service Evaluation and Selection Using Hybrid MCDM Methods

With the rapid growth of cloud services in recent years, it is very difficult to choose the suitable cloud services among those services that provide similar functionality. The non-functional quality of services is considered the most significant factor for appropriate service selection and user satisfaction in cloud computing. However, with a vast diversity in the cloud service, selection of a suitable cloud service is a very challenging task for a customer under an unpredictable environment. This study introduces a computational framework for determining the most suitable candidate cloud service by integrating the analytical hierarchical process (AHP) and Technique for order preference by similarity to ideal solution (TOPSIS). Using AHP, we define the architecture for selection process of cloud services and compute the criteria weights using pairwise comparison. Thereafter, using TOPSIS method, we obtained the final ranking of the cloud service based on overall performance. A real-time cloud case study proves the potential of our proposed framework and methodology, which demonstrates the efficacy by inducing better performance, when compared to other available cloud service selection methodologies. Finally, sensitivity analysis testifies the effectiveness and the correctness of our proposed methodology.