Automated machine learning driven quality of service management in resource-constrained software defined networks
| dc.contributor.advisor | Chavula, Josiah | |
| dc.contributor.author | White, Keegan | |
| dc.date.accessioned | 2024-06-19T07:33:38Z | |
| dc.date.available | 2024-06-19T07:33:38Z | |
| dc.date.issued | 2023 | |
| dc.date.updated | 2024-06-06T12:21:46Z | |
| dc.description.abstract | 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. | |
| dc.identifier.apacitation | White, K. (2023). <i>Automated machine learning driven quality of service management in resource-constrained software defined networks</i>. (). ,Faculty of Science ,Department of Computer Science. Retrieved from http://hdl.handle.net/11427/39923 | en_ZA |
| dc.identifier.chicagocitation | White, Keegan. <i>"Automated machine learning driven quality of service management in resource-constrained software defined networks."</i> ., ,Faculty of Science ,Department of Computer Science, 2023. http://hdl.handle.net/11427/39923 | en_ZA |
| dc.identifier.citation | White, K. 2023. Automated machine learning driven quality of service management in resource-constrained software defined networks. . ,Faculty of Science ,Department of Computer Science. http://hdl.handle.net/11427/39923 | en_ZA |
| dc.identifier.ris | TY - Thesis / Dissertation AU - White, Keegan AB - 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. DA - 2023 DB - OpenUCT DP - University of Cape Town KW - Computer Science LK - https://open.uct.ac.za PY - 2023 T1 - Automated machine learning driven quality of service management in resource-constrained software defined networks TI - Automated machine learning driven quality of service management in resource-constrained software defined networks UR - http://hdl.handle.net/11427/39923 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/39923 | |
| dc.identifier.vancouvercitation | White K. Automated machine learning driven quality of service management in resource-constrained software defined networks. []. ,Faculty of Science ,Department of Computer Science, 2023 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/39923 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Computer Science | |
| dc.publisher.faculty | Faculty of Science | |
| dc.subject | Computer Science | |
| dc.title | Automated machine learning driven quality of service management in resource-constrained software defined networks | |
| dc.type | Thesis / Dissertation | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationlevel | MSc |