An intelligent routing approach for multimedia traffic transmission over SDN

Al Jameel, Mohammed, Kanakis, Triantafyllos, Turner, Scott, Al-Sherbaz, Ali ORCID: 0000-0002-0995-1262, Bhaya, Wesam S and Al-khafajiy, Mohammed (2023) An intelligent routing approach for multimedia traffic transmission over SDN. In: 2023 15th International Conference on Developments in eSystems engineering, 9-12 January 2023, Baghdad / Anbar, Iraq. ISBN 9798350335149

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Abstract

Multimedia applications such as video streaming services have become popular, especially with the rapid growth of users, devices, increased availability and diversity of these services over the internet. In this case, service providers and network administrators have difficulties ensuring end-user satisfaction because the traffic generated by such services is more exposed to multiple network quality of service impairments, including bandwidth, delay, jitter, and loss ratio. This paper proposes an intelligent-based multimedia traffic routing framework that exploits the integration of a reinforcement learning technique with software-defined networking to explore, learn and find potential routes for video streaming traffic. Simulation results through a realistic network and under various traffic loads, demonstrate the proposed scheme's effectiveness in providing improved end-user viewing quality, higher throughput and lower video quality switches when compared to the existing techniques.

Item Type:	Conference or Workshop Item (Paper)
Article Type:	Article
Additional Information:	© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords:	Multimedia traffic; QoE; QoS; SDN; Reinforcement learning
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Schools and Research Institutes > School of Business, Computing and Social Sciences
Research Priority Areas:	Applied Business & Technology
Depositing User:	Rhiannon Goodland
Date Deposited:	24 May 2023 10:39
Last Modified:	31 Oct 2023 13:00
URI:	https://eprints.glos.ac.uk/id/eprint/12777

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