Biblio

The rapid development of cloud computing has resulted in the emergence of numerous web services on the Internet. Selecting a suitable cloud service is becoming a major problem for users especially non-professionals. Quality of Service (QoS) is considered to be the criterion for judging web services. There are several Collaborative Filtering (CF)-based QoS prediction methods proposed in recent years. QoS values among different users may vary largely due to the network and geographical location. Moreover, QoS data provided by untrusted users will definitely affect the prediction accuracy. However, most existing methods seldom take both facts into consideration. In this paper, we present a trust-aware and location-based approach for web service QoS prediction. A trust value for each user is evaluated before the similarity calculation and the location is taken into account in similar neighbors selecting. A series of experiments are performed based on a realworld QoS dataset including 339 service users and 5,825 services. The experimental analysis shows that the accuracy of our method is much higher than other CF-based methods.

In this paper, we focus on the principal-agent problems in continuous time when the participants have ambiguity on the output process in the framework of g-expectation. The first best (or, risk-sharing) type is studied. The necessary condition of the optimal contract is derived by means of the optimal control theory. Finally, we present some examples to clarify our results.