Web Service Qos Prediction Based on Autoencoder with Mini-Batch Gradient Descent

Reliability prediction of Web services has become very important in related research communities. Especially, predicting the Quality of Service (QoS) for active users has been a hot issue of research and application. On the other hand, with the rapidly growing in number of service providers and users, resulting in a large number of data sets. It has a big impact to the QoS such as managing and monitoring for describing functional and non-functional characteristics of Web services. Therefore, we will certainly struggle at processing large data sets in the future, unless the issues are resolved quickly before it happens. In that context, QoS prediction on big data set is an urgent problem to be solved. In this paper, we present a new model for handling this problem based on autoencoder, it is called autominibatch. We use this model to cope with large data sets by using Backpropagation and Mini-batch gradient descent for predicting QoS values of Web services. This also is a new method for evaluating prediction of the field of web service quality. Our experiments were performed on two data sets in the WS-DREAM data set and the experimental results have proved the effectiveness of the proposed model.