OPRC: An Online Personalized Reputation Calculation Model in Service-Oriented Computing Environments

Abstract

Cloud applications based on service-oriented architectures usually integrate many component services to implement specific application logic. In service-oriented computing environments, many Web services are provided for users to build service-oriented systems. Since the performance of the same Web service varies according to different users' perspectives, the users have to personally select the optimal Web services according to the quality-of-service (QoS) data observed by other similar users. However, users with a low reputation provide unreliable data, which has a negative impact on service selection. Moreover, the QoS data vary over time due to changes in user reputation; and therefore, how to calculate a personalized reputation for each user at runtime remains a substantial problem. To address this critical challenge, this paper proposes an online reputation calculation method, called the OPRC, to efficiently provide a personalized reputation for each user. Based on the users' observed QoS data, the OPRC employs MF and online learning techniques to calculate personalized reputations. To validate the approach, large-scale experiments are conducted, which contain two QoS attributes from 142 reliable users and 15 unreliable users. The results show that OPRC has high accuracy and effectiveness compared to other approaches.

Keywords

• Service-oriented systems
• Web services
• quality of service (QoS)
• user reputation
• online learning