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Machine Learning-based Stable P2P IPTV Overlay

Muhammad Javid Iqbal1,2, Ihsan Ullah2,*, Muhammad Ali2, Atiq Ahmed2, Waheed Noor2, Abdul Basit2

1 Directorate of Information Technology, Sardar Bahadur Khan Women’s University, Quetta, Pakistan
2 Department of Computer Science and Information Technology, University of Balochistan, Quetta, Pakistan

* Corresponding Author: Ihsan Ullah. Email: email

Computers, Materials & Continua 2022, 71(3), 5381-5397. https://doi.org/10.32604/cmc.2022.024116

Abstract

Live video streaming is one of the newly emerged services over the Internet that has attracted immense interest of the service providers. Since Internet was not designed for such services during its inception, such a service poses some serious challenges including cost and scalability. Peer-to-Peer (P2P) Internet Protocol Television (IPTV) is an application-level distributed paradigm to offer live video contents. In terms of ease of deployment, it has emerged as a serious alternative to client server, Content Delivery Network (CDN) and IP multicast solutions. Nevertheless, P2P approach has struggled to provide the desired streaming quality due to a number of issues. Stability of peers in a network is one of the major issues among these. Most of the existing approaches address this issue through older-stable principle. This paper first extensively investigates the older-stable principle to observe its validity in different scenarios. It is observed that the older-stable principle does not hold in several of them. Then, it utilizes machine learning approach to predict the stability of peers. This work evaluates the accuracy of several machine learning algorithms over the prediction of stability, where the Gradient Boosting Regressor (GBR) out-performs other algorithms. Finally, this work presents a proof-of-concept simulation to compare the effectiveness of older-stable rule and machine learning-based predictions for the stabilization of the overlay. The results indicate that machine learning-based stability estimation significantly improves the system.

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APA Style
Iqbal, M.J., Ullah, I., Ali, M., Ahmed, A., Noor, W. et al. (2022). Machine learning-based stable P2P IPTV overlay. Computers, Materials & Continua, 71(3), 5381-5397. https://doi.org/10.32604/cmc.2022.024116
Vancouver Style
Iqbal MJ, Ullah I, Ali M, Ahmed A, Noor W, Basit A. Machine learning-based stable P2P IPTV overlay. Comput Mater Contin. 2022;71(3):5381-5397 https://doi.org/10.32604/cmc.2022.024116
IEEE Style
M.J. Iqbal, I. Ullah, M. Ali, A. Ahmed, W. Noor, and A. Basit "Machine Learning-based Stable P2P IPTV Overlay," Comput. Mater. Contin., vol. 71, no. 3, pp. 5381-5397. 2022. https://doi.org/10.32604/cmc.2022.024116



cc Copyright © 2022 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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