FES: A Fast Efficient Scalable QoS Prediction Framework

• URL: http://arxiv.org/abs/2103.07494v2
• Date: Tue, 16 Mar 2021 04:11:46 GMT
• Title: FES: A Fast Efficient Scalable QoS Prediction Framework
• Authors: Soumi Chattopadhyay, Chandranath Adak, Ranjana Roy Chowdhury
• Abstract summary: One of the primary objectives of designing a prediction algorithm is to achieve satisfactory prediction accuracy. The algorithm has to be faster in terms of prediction time so that it can be integrated into a real-time recommendation system. The existing algorithms on prediction often compromise on one goal while ensuring the others.
• Score: 0.9176056742068814
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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