Real-time application clustering in wide area networks

Abstract

Network traffic classification employing Machine Learning and Statistical approaches have contributed to the understanding of the dynamic nature of traffic. For further improvement, all phases of networks, including when the requirements of the network exceed its current resources, must be considered. With scenarios of networks with low-speed links, fragmentation and loss of packets leading to poor quality of services are highly expected, resulting in few flows being classified at a time with the features extracted. Training a classifier with few features inhibits the overall classification accuracy with real-time traffic traces. We propose a Real-Time Application Clustering (R-TAC) strategy which can classify application flows utilizing the limited flow features extracted. Results from evaluation reveal that our proposed clustering approach performs better in terms of classification accuracy (96.40%) and precision metrics (85–99%) than the existing state of the art methods, and the best classification accuracy when validated with an existing dataset.