QoS prediction for network data traffic using hierarchical modified regularized least squares rough support vector regression
Arindam Chaudhuri, Soumya Maity, Soumya K. Ghosh · 2015
In this paper, we present a novel approach for predicting QoS on networks having complex traffic and multitenant architecture. We propose a soft computing based hierarchical modified regularized least squares rough support vector regression approach on network traffic to deliver better QoS. QoS prediction takes advantage of past service usage experiences. It does not consume any additional invocations of network services and avoids time consuming real world service incantations. We discuss the proposed approach and provide important aspects of QoS prediction. The experiments are conducted on real world public dataset and compared with benchmark dataset. The results show that proposed approach achieves high prediction accuracies than other techniques.