Performance analysis of a cloud-based network analytics system with multiple-source data aggregation
International Journal of Pervasive Computing and Communications
ISSN: 1742-7371
Article publication date: 26 September 2022
Issue publication date: 16 November 2023
Abstract
Purpose
The purpose of this paper is geared towards the capture and analysis of network traffic using an array ofmachine learning (ML) and deep learning (DL) techniques to classify network traffic into different classes and predict network traffic parameters.
Design/methodology/approach
The classifier models include k-nearest neighbour (KNN), multilayer perceptron (MLP) and support vector machine (SVM), while the regression models studied are multiple linear regression (MLR) as well as MLP. The analytics were performed on both a local server and a servlet hosted on the international business machines cloud. Moreover, the local server could aggregate data from multiple devices on the network and perform collaborative ML to predict network parameters. With optimised hyperparameters, analytical models were incorporated in the cloud hosted Java servlets that operate on a client–server basis where the back-end communicates with Cloudant databases.
Findings
Regarding classification, it was found that KNN performs significantly better than MLP and SVM with a comparative precision gain of approximately 7%, when classifying both Wi-Fi and long term evolution (LTE) traffic.
Originality/value
Collaborative regression models using traffic collected from two devices were experimented and resulted in an increased average accuracy of 0.50% for all variables, with a multivariate MLP model.
Keywords
Acknowledgements
The authors would like to thank the University of Mauritius for providing the necessary facilities for conducting this research.
Conflict of interest: On behalf of all authors, the corresponding author states that there is no conflict of interest.
Citation
Fowdur, T.P. and Babooram, L. (2023), "Performance analysis of a cloud-based network analytics system with multiple-source data aggregation", International Journal of Pervasive Computing and Communications, Vol. 19 No. 5, pp. 698-733. https://doi.org/10.1108/IJPCC-06-2022-0244
Publisher
:Emerald Publishing Limited
Copyright © 2022, Emerald Publishing Limited