A new method of QoS prediction based on probabilistic latent feature analysis and cloud similarity

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.

Distributed diagnosis based on distributed probability propagation nets

“…Another work (Cardoso et al, 1999) integrates uncertainty reasoning using possibility logic with Petri nets, which was more elaborated in Lee et al (2003). Furthermore, and even aside from diagnostic purposes, probabilistic reasoning is being used in a variety of fields, such as cyber-security (Xu et al, 2016;Han et al, 2016), performance modelling (Zhang et al, 2016) and prediction (Lu et al, 2016).…”