A comparative study of QoS ranking prediction techniques in cloud services
Shirish Nagar, Jyotirmoy Karjee · 2016
Quality of Service (QoS) is developing as an important parameter to describe Web services for cloud users and service providers. The analysis of QoS issues in web services by service providers and user is essential for optimization and improvement. As the static prediction approaches (like arithmetic and average value methods) are incapable of capturing non-linearity in QoS data, the dynamic prediction methods like collaborative filtering, similarity measure and multi-dimensional weighting are used. This paper aims to present a comparative study of different intelligent techniques employed to improve the prediction of QoS Ranking for a cloud service. The simulation of the experiment and validation of the observations and results is conducted through MATLAB® toolbox ANFISEdit.