Fuzzy Hybrid Approach for Ranking and Selecting Services in Cloud-based Marketplaces

Abstract

Background and Objective: The popularity cloud computing has led to the proliferation of services that are commoditized and traded on cloud e-marketplaces. Besides, user’s cloud service requirements-QoS preferences and aspiration are often shrouded in vagueness and subjectivity. Therefore, cloud service selection can be overwhelming and lead to service choice overload. Existing cloud service selection approaches rarely provide mechanisms to elicit both the QoS preferences and aspirations, but rather considers either of them. This study aimed to design fuzzy-based model for service selection in e-market places that articulates both QoS preferences and aspirations. Materials and Methods: This model comprised a fuzzy Analytic Hierarchy Process (AHP) method for deriving relative priority weights of QoS attributes, a fuzzy decision-making method for obtaining user’s QoS aspiration values and a fuzzy multi-objective optimization module for evaluating the services with respect to user requirements. A simulated experiment was conduct using publicly QoS dataset and ranking accuracy produced by the proposed approach compared to existing methods was measured using Normalize Discounted Cumulative Gain (NCDG) metric. Results: The descriptive and inferential analyses of the ranking results from both versions of the proposed approach produce better accuracy results based on the NCDG metric and were in all cases closer to the benchmark metric than the other two existing methods used in this simulation. Conclusion: Results from current simulation experiment showed that the ranking accuracy of this model is not compromised by subjective QoS information from users and this approach is applicable use the subjective QoS requirements of user’s in ranking services in the cloud e-marketplaces.