Towards an Effective QoS Prediction of Web Services using Context-Aware Dynamic Bayesian Network Model

• Main Authors: Vijay Bhanu Srinivasan, Saravanan Chandrasekaran, Latha Parthiban
• Format: Article
• Language: English
• Published: Faculty of Mechanical Engineering in Slavonski Brod, Faculty of Electrical Engineering in Osijek, Faculty of Civil Engineering in Osijek 2018-01-01
• Series: Tehnički Vjesnik
• Subjects: contextual information; Dynamic Bayesian Network; QoS; QoS normalization; Web Service; WS-Discovery
• Online Access: https://hrcak.srce.hr/file/302789
• ISSN: 1330-3651; 1848-6339

The functionally equivalent web services (WSs) with different quality of service (QoS) leads to WS discovery models to identify the optimal WS. Due to the unpredictable network connections and user environment, the predicted values of the QoS are likely to fluctuate. The proposed Context-Aware Bayesian Network (CABN) system overcomes these limitations by incorporating the contextual factors in user, server, and environmental perspective. In this paper, three components are introduced for personalized QoS prediction. First, the CABN incorporates the pre-clustering model and reduces the searching space for QoS prediction. Second, the CABN confronts with the multi-constraint problem while considering the multi-dimensional QoS parameters of similar QoS data in WS discovery. Third, the CABN sends the normalized QoS value of records in similar as well as neighbor clusters as inputs to the Dynamic Bayesian Network and improves the prediction accuracy. The experimental results prove that the proposed CABN achieves better WS-Discovery than the existing work within a reasonable time.