Fuzzy Hybrid Approach for Ranking and Selecting Services in Cloud-based Marketplaces
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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.
Keywords
Q Science (General), QA75 Electronic computers. Computer science