Original scientific paper
https://doi.org/10.17559/TV-20161104072515
Towards an Effective QoS Prediction of Web Services using Context-Aware Dynamic Bayesian Network Model
Saravanan Chandrasekaran
orcid.org/0000-0003-2955-1917
; Department of Computer Science and Engineering, Annamalai University, Sadagopan Nagar, Annamalai Nagar, Chidambaram, Tamil Nadu 608002, India
Vijay Bhanu Srinivasan
; Department of Computer Science and Engineering, Annamalai University, Tamil Nadu, India
Latha Parthiban
; Department of Computer Science, Pondicherry University Pondicherry, India
Abstract
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.
Keywords
contextual information; Dynamic Bayesian Network; QoS; QoS normalization; Web Service; WS-Discovery
Hrčak ID:
205916
URI
Publication date:
22.9.2018.
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