Lie Qu

Evaluating Cloud Users’ Credibility of Providing Subjective Assessment or Objective Assessment for Cloud Services

This paper proposes a novel model for evaluating cloud users’ credibility of providing subjective assessment or objective assessment for cloud services. In contrast to prior studies, cloud users in our model are divided into two classes, i.e., ordinary cloud consumers providing subjective assessments and professional testing parties providing objective assessments. By analyzing and comparing subjective assessments and objective assessments of cloud services, our proposed model can not only effectively evaluate the trustworthiness of cloud consumers and reputations of testing parties on how truthfully they assess cloud services, but also resist user collusion to some extent. The experimental results demonstrate that our model significantly outperforms existing work in both the evaluation of users’ credibility and the resistance of user collusion.

An Uncertain Assessment Compatible Incentive Mechanism for Eliciting Continual and Truthful Assessments of Cloud Services

The evaluation of dynamic performance of cloud services relies on continual assessments from cloud users, e.g., ordinary consumers and testing parties. In order to elicit continual and truthful assessments, an effective incentive mechanism in cloud environments should allow users to provide uncertain assessments when they are not sure about the real performance of cloud services, e.g., when users do not access cloud services on time, rather than providing untruthful or arbitrary assessments. Different from all prior works, we propose a novel uncertain assessment compatible incentive mechanism. Under this mechanism, a user not only has sufficient incentives to continually provide truthful assessments, but also would prefer providing uncertain assessments over untruthful or arbitrary assessments since uncertain assessments can bring more benefits than untruthful or arbitrary assessments. We theoretically analyze the proposed incentive mechanism and evaluate it through simulations under different circumstances. The theoretical analysis demonstrates the effectiveness of our approach. Moreover, the experimental results based on simulations strongly support the results from the theoretical analysis.

Confidence-Aware Reputation Bootstrapping in Composite Service Environments

We propose a novel reputation bootstrapping approach for both composite and atomic services in service-oriented environments. We consider multiple factors which may implicitly represent reputations of new services. Our approach does not rely on empirical assumptions. In contrast, we propose a data-driven method to determine how much a factor can represent service reputation. The reputation-related factors are modelled in a layer-based framework. This aims to quantitatively describe the importance of factors in reputation bootstrapping. Furthermore, we define confidence to represent how reliable the bootstrapped reputation of a new service is. We evaluate our approach based on a real-world dataset. The experimental results demonstrate the feasibility and outperformance of our approach.