Title: A new method of QoS prediction based on probabilistic latent feature analysis and cloud similarity
Authors: Weina Lu; Xiaohui Hu; Xiaotao Li; Yuan Wei
Addresses: School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China; School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, 300222, China ' School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China ' School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China ' Ming Safety Technology Branch of China Coal Research Institute, Beijing, 100013, China
Abstract: With the increasing requirements of service mode in cloud computing, predicting accurate quality of service (QoS) is greatly significant in the recommender or composition system to avoid expensive and time-consuming invocations. Unlike previous research approaches which generally stay on the explicit values of QoS data, in this paper, we propose a new prediction method based on probabilistic latent feature analysis and cloud similarity. As user experience quality of service is influenced by the implicit factors, such as network performance, user context and user preference, we first consider these factors as the latent features of user and relate it to the QoS data using pLSA model. Then, the users or services are clustered based on the similar latent features. Finally, after mining the similarity of users in the same cluster by cloud model, the personalised QoS values are predicted by the experience quality of the similar users with the similar services. Experiment results with a real QoS dataset show that the proposed approach can effectively achieve an accurate QoS prediction.
Keywords: quality of service; QoS prediction; user experience; probabilistic latent features; cloud models; cloud computing; cloud similarity; cloud services; network performance; user context; user preferences; personalisation.
International Journal of High Performance Computing and Networking, 2016 Vol.9 No.1/2, pp.52 - 60
Available online: 11 Feb 2016 *Full-text access for editors Access for subscribers Purchase this article Comment on this article