Abstract
Service-oriented architecture (SOA) is an approach for constructing distributed business-driven frameworks. Reliability of service- oriented systems (SOS) relies on the Web services used and in addition Internet associations. Predicting reliability of Web service has turned into an imperative exploration issue. In this paper Hidden Markov Model (HMM) and Artificial Neural Network (ANN) are used to model the Web service failure model and predict Web services reliability. The forward-backward estimation-maximization algorithm is used to estimate the modeling parameters for HMM. Different methods of ANN like feed-forward, multilayer perceptron, and radial basis function neural networks are applied to predict the reliability. The accuracy for each model is calculated and the performance parameters of models are compared by considering Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) metrics.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
