REDO: a reinforcement learning-based dynamic routing algorithm selection method for SDN
Conference paper
Al-Jawad, A., Comsa, I., Shah, P., Gemikonakli, O. and Trestian, R. 2021. REDO: a reinforcement learning-based dynamic routing algorithm selection method for SDN. 2021 IEEE NFV-SDN. Virtual Conference 09 - 11 Nov 2021 IEEE. pp. 54-59 https://doi.org/10.1109/NFV-SDN53031.2021.9665140
Type | Conference paper |
---|---|
Title | REDO: a reinforcement learning-based dynamic routing algorithm selection method for SDN |
Authors | Al-Jawad, A., Comsa, I., Shah, P., Gemikonakli, O. and Trestian, R. |
Abstract | The current increase in the Internet traffic along with the global crisis have accelerated the roll-out of the next generation 5G network and key enabling technologies. In this context, addressing the end-to-end Quality of Service (QoS) provisioning in order to guarantee a sustainable service delivery to the end-users became of paramount importance. Some of the enabling technologies that could play a key role in this regard are Software Defined Network (SDN) and Machine Learning (ML). This paper proposes REDO, a Reinforcement lEarning-based Dynamic rOuting algorithm selection method that decides on the conventional routing algorithm to be applied on the traffic flows within a SDN environment. REDO will dynamically select the most appropriate routing algorithm from a set of centralized routing algorithms (MHA, WSP, SWP, MIRA) that maximizes the reward function from the network. The proposed REDO solution is implemented and evaluated using an experimental setup based on Mininet, Floodlight controller and Open vSwitch switches. The results show that REDO outperforms other state-of-the-art solutions. |
Conference | 2021 IEEE NFV-SDN |
Page range | 54-59 |
ISBN | |
Electronic | 9781665439831 |
Paperback | 9781665439848 |
Publisher | IEEE |
Publication dates | |
09 Nov 2021 | |
Online | 05 Jan 2022 |
Publication process dates | |
Deposited | 20 Oct 2021 |
Accepted | 24 Sep 2021 |
Output status | Published |
Accepted author manuscript | |
Copyright Statement | © 2021 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 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/NFV-SDN53031.2021.9665140 |
Language | English |
Book title | 2021 IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN) |
https://repository.mdx.ac.uk/item/8988q
Download files
61
total views42
total downloads4
views this month2
downloads this month