Automated machine learning driven quality of service management in resource-constrained software defined networks

Community networks are a means to bridge the connectivity gaps present in low-income and rural areas. Many of these networks are resource-constrained, mesh-based, and connected to the Internet via low-capacity links. These characteristics result in poor network performance. Software Defined Networking facilitates dynamic resource allocation to address real-time network degradation. Using the Software Defined Networking paradigm, methods to identify what traffic to allocate resources to offer a promising solution to common network issues in community networks. This dissertation presents a novel end-toend framework that uses deep learning models to facilitate real-time resource allocation in a resource-constrained network based on heuristics for traffic prioritisation. The deep learning models utilised by the framework are trained on data gathered from a community network and extensively tested in online network simulations. The results of this study convey that deep learning enabled Software Defined Networks can improve network throughput and decrease packet loss in real-time, thus improving network Quality of Service.

Grantor

Department of Computer Science

Year dc:date.issued

2023

Author dc:creator

• White, Keegan

Advisor dc:contributor.advisor

• Chavula, Josiah

• Computer Science

Handle dc:identifier.uri

http://hdl.handle.net/11427/39923

OAI identifier oai:identifier

oai:open.uct.ac.za:11427/39923