Abstract-Cloud computing has been attracting huge attention in recent years. More and more individuals and organizations have been moving their work into cloud environments because of its flexibility and low-cost. Due to the emergence of a variety of cloud service providers, selecting the most suitable cloud service becomes increasingly important for potential cloud users. In prior studies, the selection and comparison of cloud services usually focus on objective performance analysis based on cloud monitoring and benchmark testing without considering the viewpoints of cloud users who are indeed consuming cloud services. This causes a problem that some vital aspects which concern cloud consumers (e.g., privacy and cloud providers' reputation) are not taken into account in cloud service selection. In this paper, we propose a novel model of cloud service selection by aggregating the information from both the feedback from cloud users and objective performance analysis from a trusted third party. Based on this model, we first propose a framework which supports our cloud service selection approach. Then after classifying subjective assessment and objective assessment, we present a novel cloud service selection approach to aggregate all subjective assessments and objective assessments through a fuzzy simple additive weighting system. In addition, to reduce the bias caused by unreasonable feedback from unprofessional or malicious cloud users, a method is proposed for filtering the feedback from such users. After processing, the aggregated result can quantitatively reflect the overall quality of a cloud service. Finally, a case study is presented to illustrate the advantages of our approach.
To reduce the bias caused by unreasonable feedback from unprofessional or malicious cloud users, a method was proposed for filtering the feedback from such users in REF .
Cloud Service Selection Based on the Aggregation of User Feedback and Quantitative Performance Assessment
2013 IEEE International Conference on Services Computing
2013 IEEE International Conference on Services Computing
Computer Science