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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 Networki...
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| Format: | Thesis |
| Language: | English |
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Department of Computer Science
2024
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| _version_ | 1867614056186118144 |
|---|---|
| access_status_str | Open Access |
| author | White, Keegan |
| author2 | Chavula, Josiah |
| author_browse | Chavula, Josiah White, Keegan |
| author_facet | Chavula, Josiah White, Keegan |
| author_sort | White, Keegan |
| collection | Thesis |
| description | 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. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/39923 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:45:58.344Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Department of Computer Science |
| publisherStr | Department of Computer Science |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/39923 Automated machine learning driven quality of service management in resource-constrained software defined networks White, Keegan Chavula, Josiah Computer Science 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. 2024-06-19T07:33:38Z 2024-06-19T07:33:38Z 2023 2024-06-06T12:21:46Z Thesis / Dissertation Masters MSc http://hdl.handle.net/11427/39923 eng application/pdf Department of Computer Science Faculty of Science |
| spellingShingle | Computer Science White, Keegan Automated machine learning driven quality of service management in resource-constrained software defined networks |
| thesis_degree_str | Master's |
| title | Automated machine learning driven quality of service management in resource-constrained software defined networks |
| title_full | Automated machine learning driven quality of service management in resource-constrained software defined networks |
| title_fullStr | Automated machine learning driven quality of service management in resource-constrained software defined networks |
| title_full_unstemmed | Automated machine learning driven quality of service management in resource-constrained software defined networks |
| title_short | Automated machine learning driven quality of service management in resource-constrained software defined networks |
| title_sort | automated machine learning driven quality of service management in resource constrained software defined networks |
| topic | Computer Science |
| url | http://hdl.handle.net/11427/39923 |
| work_keys_str_mv | AT whitekeegan automatedmachinelearningdrivenqualityofservicemanagementinresourceconstrainedsoftwaredefinednetworks |