A Fuzzy-Multi Attribute Decision Making approach for efficient service selection in cloud environments

Abstract

In general, objective and subjective QoS assessment data forms the major data source for service evaluations. Especially, in the most dynamic service-oriented computing ecosystem like cloud environments, such assessment data is subjected to high uncertainty due to the dynamics in the number of cloud services and related QoS values. In such cases, the accuracy and reliability of the most efficient cloud service selection model remain questionable. Further, the complex interdependencies among the multiple QoS parameters and the trustworthiness of the cloud services complicate the process of handling uncertainty during cloud service selection. This work presents Fuzzy-Multi Attribute Decision Making (FMADM), a hybrid trust prediction model to provide accurate trust-based cloud service evaluations while efficiently handling the uncertainty in the cloud service assessment data. FMADM employs (i) Picture Fuzzy Sets (PFSs): to capture inconsistency, uncertainty, and ambiguity in the QoS data, (ii) Naïve Bayes: to recompute the weights of the QoS attributes, (iii) Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS): to model the non-linear relationship between the QoS values of cloud services and their corresponding trust result, and (iv) Random Forest Classifier (RFC): to predict the trust value of cloud services. The performance of FMADM was evaluated using Quality Web Service (QWS) v1.0 dataset under different scenarios using various quality metrics.