Online Optimization of Collaborative Web Service QoS Prediction Based on Approximate Dynamic Programming

More recently, with the increasing demand of web services on the World Wide Web used in the Internet of Things (IoTs), there has been a growing interest in the study of efficient web service quality evaluation approaches through the use of prediction strategies to obtain accurate quality-of-service (QoS) values. The unpredictable network environment imposes very challenging obstacles to web service QoS prediction. Most of the traditional web service QoS prediction approaches are implemented only using a set of static model parameters with the help of designer´s priori knowledge. Unlike the traditional QoS prediction approaches, our algorithm in this paper is realized by incorporating approximate dynamic programming (ADP)-based online parameter tuning strategy into the QoS prediction approach. Through online learning and optimization, the proposed approach implements the QoS prediction with automatic parameter tuning capability, and the prior knowledge or identification of the prediction model is not required. The near-optimal performance of QoS prediction can therefore be achieved. Experiment studies are carried out to demonstrate the effectiveness of the proposed ADP-based prediction approach.