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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.

Abstract-Due to the diversity and dynamics of cloud services, it is usually hard for potential cloud consumers to select the most suitable cloud service. In prior studies, cloud service selection is usually based on either objective performance assessment or cloud users' subjective assessment (e.g., subjective ratings). However, either assessment way has its limitation in reflecting the quality of cloud services. This causes a problem that some vital performance aspects which concern potential cloud consumers are not taken into account in cloud service selection.This paper proposes a novel context-aware cloud service selection model based on the comparison and aggregation of subjective assessment extracted from cloud user feedback and objective assessment from quantitative performance testing. In this model, objective assessment provided by some professional testing parties is used as a benchmark to filter out potentially biased subjective assessment from cloud users, then objective assessment and subjective assessment are aggregated to evaluate the overall performance of cloud services according to potential cloud users' personalized requests. Moreover, our model takes the contexts of objective assessment and subjective assessment into account. By calculating the similarity between different contexts, the benchmark level of objective assessment is dynamically adjusted according to context similarity, which makes the following comparison and aggregation process more accurate and effective. After aggregation, the final results can quantitatively reflect the overall quality of cloud services. Finally, our proposed model is evaluated through the experiments executed in different conditions.

Due to the diversity and dynamic nature of cloud services, it is usually hard for potential cloud consumers to select the most suitable cloud service. This paper proposes CCCloud: a context-aware and credible cloud service selection model based on the comparison and aggregation of subjective assessments extracted from ordinary cloud consumers and objective assessments from quantitative performance testing parties. We propose a novel approach to evaluate cloud users' credibility, which not only can accurately evaluate how truthfully they assess cloud services, but also resist user collusion. In addition, in our model, objective assessments are used as benchmarks to filter out potentially biased subjective assessments, and then objective assessments and subjective assessments are aggregated to evaluate the overall performance of a cloud service. Furthermore, our model takes the contexts of objective assessments and subjective assessments into account. By calculating the similarity between different contexts, the benchmark level of objective assessments is dynamically adjusted according to context similarity, and the aggregated final scores of alternative cloud services are weighted by the similarity between the contexts of a potential cloud consumer and every testing party. This makes our cloud service selection model reflect potential cloud consumers' customized requirements more effectively. Finally, our proposed model is evaluated through the experiments conducted under different conditions. The experimental results demonstrate that our model significantly outperforms the existing work, especially in the resistance of user collusion.