FES: A Fast Efficient Scalable QoS Prediction Framework

by   Soumi Chattopadhyay, et al.

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.


page 1

page 5


CAHPHF: Context-Aware Hierarchical QoS Prediction with Hybrid Filtering

With the proliferation of Internet-of-Things and continuous growth in th...

TPMCF: Temporal QoS Prediction using Multi-Source Collaborative Features

Recently, with the rapid deployment of service APIs, personalized servic...

Summary of a Literature Review in Scalability of QoS-aware Service Composition

This paper shows that authors have no consistent way to characterize the...

Outlier Resilient Collaborative Web Service QoS Prediction

Nowadays, more and more Web services are provided by different enterpris...

A Probability Distribution and Location-aware ResNet Approach for QoS Prediction

In recent years, the number of online services has grown rapidly, invoke...

β-Divergence-Based Latent Factorization of Tensors model for QoS prediction

A nonnegative latent factorization of tensors (NLFT) model can well mode...

Probabilistic Deep Supervision Network: A Noise-Resilient Approach for QoS Prediction

Quality of Service (QoS) prediction is an essential task in recommendati...

Please sign up or login with your details

Forgot password? Click here to reset