FES: A Fast Efficient Scalable QoS Prediction Framework
  Quality-of-Service prediction of web service is an integral part of services
computing due to its diverse applications in the various facets of a service
life cycle, such as service composition, service selection, service
recommendation. One of the primary objectives of designing a QoS prediction
algorithm is to achieve satisfactory prediction accuracy. However, accuracy is
not the only criteria to meet while developing a QoS prediction algorithm. The
algorithm has to be faster in terms of prediction time so that it can be
integrated into a real-time recommendation or composition system. The other
important factor to consider while designing the prediction algorithm is
scalability to ensure that the prediction algorithm can tackle large-scale
datasets. The existing algorithms on QoS prediction often compromise on one
goal while ensuring the others. In this paper, we propose a semi-offline QoS
prediction model to achieve three important goals simultaneously: higher
accuracy, faster prediction time, scalability. Here, we aim to predict the QoS
value of service that varies across users. Our framework consists of
multi-phase prediction algorithms: preprocessing-phase prediction, online
prediction, and prediction using the pre-trained model. In the preprocessing
phase, we first apply multi-level clustering on the dataset to obtain
correlated users and services. We then preprocess the clusters using
collaborative filtering to remove the sparsity of the given QoS invocation log
matrix. Finally, we create a two-staged, semi-offline regression model using
neural networks to predict the QoS value of service to be invoked by a user in
real-time. Our experimental results on four publicly available WS-DREAM
datasets show the efficiency in terms of accuracy, scalability, fast
responsiveness of our framework as compared to the state-of-the-art methods.
