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.
Highlights
Service-oriented computing (SOC) provides a componentbased computing paradigm by dynamically integrating its component systems through service composition [1]
The reputation calculation model that we construct follows the unified framework presented by Ling et al [25], which contains a prediction model, penalty function and link function. This framework was proposed for a rating system, we provide a novel online reputation calculation model by utilizing its flexibility, and we demonstrate that it works well on Web services
Inspired by the framework of rating systems [25], we introduce an online personalized reputation calculation model in the SOC environment and demonstrate its applicability in terms of the response time (RT) and throughput (TP)
Summary
Service-oriented computing (SOC) provides a componentbased computing paradigm by dynamically integrating its component systems through service composition [1]. Wang et al [12] proposed a reputation evaluation approach for Web service selection, and their reputation evaluation model included feedback checking, feedback adjustment, and feedback detection. They used QoS values to calculate user reputation through a statistical average approach. Li et al [16] proposed a reputation measurement approach based on a user’s context by calculating and weakening the effect caused by the user’s context They employed collaborative filtering to measure the reputation of each Web service. These approaches can be effective under offline conditions; most of these works did not consider online conditions
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