Improving Pan-African research and education networks through traffic engineering: A LISP/SDN approach

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dc.contributor.advisorSuleman, Husseinen_ZA
dc.contributor.advisorDensmore, Melissaen_ZA
dc.contributor.authorChavula, Josiahen_ZA
dc.date.accessioned2018-01-25T14:08:47Z
dc.date.available2018-01-25T14:08:47Z
dc.date.issued2017en_ZA
dc.description.abstractThe UbuntuNet Alliance, a consortium of National Research and Education Networks (NRENs) runs an exclusive data network for education and research in east and southern Africa. Despite a high degree of route redundancy in the Alliance's topology, a large portion of Internet traffic between the NRENs is circuitously routed through Europe. This thesis proposes a performance-based strategy for dynamic ranking of inter-NREN paths to reduce latencies. The thesis makes two contributions: firstly, mapping Africa's inter-NREN topology and quantifying the extent and impact of circuitous routing; and, secondly, a dynamic traffic engineering scheme based on Software Defined Networking (SDN), Locator/Identifier Separation Protocol (LISP) and Reinforcement Learning. To quantify the extent and impact of circuitous routing among Africa's NRENs, active topology discovery was conducted. Traceroute results showed that up to 75% of traffic from African sources to African NRENs went through inter-continental routes and experienced much higher latencies than that of traffic routed within Africa. An efficient mechanism for topology discovery was implemented by incorporating prior knowledge of overlapping paths to minimize redundancy during measurements. Evaluation of the network probing mechanism showed a 47% reduction in packets required to complete measurements. An interactive geospatial topology visualization tool was designed to evaluate how NREN stakeholders could identify routes between NRENs. Usability evaluation showed that users were able to identify routes with an accuracy level of 68%. NRENs are faced with at least three problems to optimize traffic engineering, namely: how to discover alternate end-to-end paths; how to measure and monitor performance of different paths; and how to reconfigure alternate end-to-end paths. This work designed and evaluated a traffic engineering mechanism for dynamic discovery and configuration of alternate inter-NREN paths using SDN, LISP and Reinforcement Learning. A LISP/SDN based traffic engineering mechanism was designed to enable NRENs to dynamically rank alternate gateways. Emulation-based evaluation of the mechanism showed that dynamic path ranking was able to achieve 20% lower latencies compared to the default static path selection. SDN and Reinforcement Learning were used to enable dynamic packet forwarding in a multipath environment, through hop-by-hop ranking of alternate links based on latency and available bandwidth. The solution achieved minimum latencies with significant increases in aggregate throughput compared to static single path packet forwarding. Overall, this thesis provides evidence that integration of LISP, SDN and Reinforcement Learning, as well as ranking and dynamic configuration of paths could help Africa's NRENs to minimise latencies and to achieve better throughputs.en_ZA
dc.identifier.apacitationChavula, J. (2017). <i>Improving Pan-African research and education networks through traffic engineering: A LISP/SDN approach</i>. (Thesis). University of Cape Town ,Faculty of Science ,Faculty Science: ICTC4D. Retrieved from http://hdl.handle.net/11427/27021en_ZA
dc.identifier.chicagocitationChavula, Josiah. <i>"Improving Pan-African research and education networks through traffic engineering: A LISP/SDN approach."</i> Thesis., University of Cape Town ,Faculty of Science ,Faculty Science: ICTC4D, 2017. http://hdl.handle.net/11427/27021en_ZA
dc.identifier.citationChavula, J. 2017. Improving Pan-African research and education networks through traffic engineering: A LISP/SDN approach. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Chavula, Josiah AB - The UbuntuNet Alliance, a consortium of National Research and Education Networks (NRENs) runs an exclusive data network for education and research in east and southern Africa. Despite a high degree of route redundancy in the Alliance's topology, a large portion of Internet traffic between the NRENs is circuitously routed through Europe. This thesis proposes a performance-based strategy for dynamic ranking of inter-NREN paths to reduce latencies. The thesis makes two contributions: firstly, mapping Africa's inter-NREN topology and quantifying the extent and impact of circuitous routing; and, secondly, a dynamic traffic engineering scheme based on Software Defined Networking (SDN), Locator/Identifier Separation Protocol (LISP) and Reinforcement Learning. To quantify the extent and impact of circuitous routing among Africa's NRENs, active topology discovery was conducted. Traceroute results showed that up to 75% of traffic from African sources to African NRENs went through inter-continental routes and experienced much higher latencies than that of traffic routed within Africa. An efficient mechanism for topology discovery was implemented by incorporating prior knowledge of overlapping paths to minimize redundancy during measurements. Evaluation of the network probing mechanism showed a 47% reduction in packets required to complete measurements. An interactive geospatial topology visualization tool was designed to evaluate how NREN stakeholders could identify routes between NRENs. Usability evaluation showed that users were able to identify routes with an accuracy level of 68%. NRENs are faced with at least three problems to optimize traffic engineering, namely: how to discover alternate end-to-end paths; how to measure and monitor performance of different paths; and how to reconfigure alternate end-to-end paths. This work designed and evaluated a traffic engineering mechanism for dynamic discovery and configuration of alternate inter-NREN paths using SDN, LISP and Reinforcement Learning. A LISP/SDN based traffic engineering mechanism was designed to enable NRENs to dynamically rank alternate gateways. Emulation-based evaluation of the mechanism showed that dynamic path ranking was able to achieve 20% lower latencies compared to the default static path selection. SDN and Reinforcement Learning were used to enable dynamic packet forwarding in a multipath environment, through hop-by-hop ranking of alternate links based on latency and available bandwidth. The solution achieved minimum latencies with significant increases in aggregate throughput compared to static single path packet forwarding. Overall, this thesis provides evidence that integration of LISP, SDN and Reinforcement Learning, as well as ranking and dynamic configuration of paths could help Africa's NRENs to minimise latencies and to achieve better throughputs. DA - 2017 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2017 T1 - Improving Pan-African research and education networks through traffic engineering: A LISP/SDN approach TI - Improving Pan-African research and education networks through traffic engineering: A LISP/SDN approach UR - http://hdl.handle.net/11427/27021 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/27021
dc.identifier.vancouvercitationChavula J. Improving Pan-African research and education networks through traffic engineering: A LISP/SDN approach. [Thesis]. University of Cape Town ,Faculty of Science ,Faculty Science: ICTC4D, 2017 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/27021en_ZA
dc.language.isoengen_ZA
dc.publisher.departmentFaculty Science: ICTC4Den_ZA
dc.publisher.facultyFaculty of Scienceen_ZA
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherResearch Networksen_ZA
dc.subject.otherEducation Networksen_ZA
dc.titleImproving Pan-African research and education networks through traffic engineering: A LISP/SDN approachen_ZA
dc.typeDoctoral Thesis
dc.type.qualificationlevelDoctoral
dc.type.qualificationnamePhDen_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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