Using SDN and Reinforcement Learning for Traffic Engineering in UbuntuNet Alliance

Chavula, Josiah and Suleman, Hussein and Densmore, Melissa (2016) Using SDN and Reinforcement Learning for Traffic Engineering in UbuntuNet Alliance, Proceedings of 3rd International Conference on Advances in Computing and Communication Engineering (ICACCE 2016), 28-29 November 2016, Durban, South Africa.

PDF ICACCE_2016_paper_102.pdf Download (237kB)

Abstract

Software Defined Networking (SDN) provides opportunities for dynamic and flexible traffic engineering. This paper discusses how UbuntuNet Alliance National Research and Education Networks (NRENs) can improve bandwidth utilization and reduce inter-NREN latencies through implementation of SDN-based traffic engineering and applying network metrics in selection of inter-NREN paths. Additionally, the paper looks at the utility of applying Reinforcement Learning to path selection, using network data obtained through an SDN controller. Results from simulations using the UbuntuNet topology show an increase in total throughputs when multipath is employed. Furthermore, simulation results show that where latency is the key metric for computing rewards, lower latencies are achieved.

Item Type:	Conference paper
Subjects:	Computer systems organization > Architectures > Distributed architectures
Date Deposited:	17 Feb 2017
Last Modified:	10 Oct 2019 15:32
URI:	http://pubs.cs.uct.ac.za/id/eprint/1162

Actions (login required)

View Item