Kaile Zhou

Decision support for personalized cloud service selection through multi-attribute trustworthiness evaluation.

Facing a customer market with rising demands for cloud service dependability and security, trustworthiness evaluation techniques are becoming essential to cloud service selection. But these methods are out of the reach to most customers as they require considerable expertise. Additionally, since the cloud service evaluation is often a costly and time-consuming process, it is not practical to measure trustworthy attributes of all candidates for each customer. Many existing models cannot easily deal with cloud services which have very few historical records. In this paper, we propose a novel service selection approach in which the missing value prediction and the multi-attribute trustworthiness evaluation are commonly taken into account. By simply collecting limited historical records, the current approach is able to support the personalized trustworthy service selection. The experimental results also show that our approach performs much better than other competing ones with respect to the customer preference and expectation in trustworthiness assessment.

QoS-aware resource matching and recommendation for cloud computing systems

Resource matching and recommendation is an important topic in the field of cloud computing. While a lot of cloud resource discovery and negotiation models have been proposed, resource matching and recommendation issues have often been neglected, such as the utilization of attribute weights and the collaborative application of empirical data, price utility and so on. To cope with this challenge, we focus on designing a novel resource recommendation method which can regulate multi-attribute matching between provider solutions and customer demands in this paper. At first, we describe a resource matching algorithm that considers both functional requirements and QoS attributes. Then, we propose a resource recommendation method for cloud computing system that integrates price utility, multi-attribute matching metric and group customer evaluation. Finally, the extensive simulation results demonstrate that our proposed method is effective in various simulated scenarios. Current results are of high significance to design an efficient resource matching and recommendation with guaranteed QoS requirements under the realistic cloud computing circumstances.

Multi-agent simulation-based residential electricity pricing schemes design and user selection decision-making

Multi-agent system employs the functions of communication, coordination and cooperation among intelligent agents to help people judge and analyze complex phenomena that cannot be directly observed, and it has become an important tool for solving large-scale complex problems. The problem of demand response (DR) in electric power system is difficult to be modeled due to the complicated environment and continuously evolving subjects. Multi-agent system can simulate the operation mechanism of electric power system, thus playing an important role in solving the DR problems. In this study, based on multi-agent simulation, we establish a multi-agent model of residential power market and propose a satisfaction function of residential users about electricity price. We focus on the interaction process among all the agents of power supply side, selling side and demand side and conduct simulation to obtain the selection and decision-making of residential users on different electricity pricing schemes. The results show that multi-agent system is beneficial to analyze, simulate and solve the DR problem in power market. Also, the satisfaction function of residential users on electricity price can support power selling enterprise to better understand the intention of residential users when choosing electricity pricing schemes and participating in DR program.