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An analysis of internet traffic flow in SANReN using active and passive measurements
National research and education networks (NRENs) in developing regions such as Africa experience various performance issues due to inadequate infrastructure and resources. The South African National Research Network (SANReN) connects universities, research institutions, and oversees science projects...
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| Format: | Thesis |
| Language: | English |
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Department of Computer Science
2022
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| _version_ | 1867614449682087936 |
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| access_status_str | Open Access |
| author | Salie, Luqmaan |
| author2 | Chavula, Josiah |
| author_browse | Chavula, Josiah Salie, Luqmaan |
| author_facet | Chavula, Josiah Salie, Luqmaan |
| author_sort | Salie, Luqmaan |
| collection | Thesis |
| description | National research and education networks (NRENs) in developing regions such as Africa experience various performance issues due to inadequate infrastructure and resources. The South African National Research Network (SANReN) connects universities, research institutions, and oversees science projects such as the Square Kilometre Array. In this study, we conduct active and passive measurements to assess the performance of SANReN and to identify problem areas in the network. Active measurements were done to determine network performance when accessing SANReN internally (using PerfSONAR) and externally (using Speedchecker). We found that SANReN needs to be reinforced in and around Port Elizabeth, Cape Town, and Durban. Universities in these cities had the highest delays and page load times. We found that the network traffic flowing from PE uses circuitous routes to flow to universities in Johannesburg and Pretoria, causing high delays (medians of 25.26 ms to WITS, 25.47 ms to UJ, and 25.95 ms to UNISA) and high page load times (medians of 237.07 ms to WITS, 272.09 ms to UNISA, 280.47 ms to UJ transferring 31594 bytes of data). Using Cape Town as the traffic source resulted in a low median throughput of 5.47 Gbps for internal active measurements. Throughput from Durban to Cape Town was low as well (4.91 Gbps), causing high page load times between these two cities (medians of 350.32 and 305.22 ms from Durban to UCT and UWC respectively). SANReN's passive measurements results show us that there is a ratio of 11.16:1 for download speed to upload speed. We also observe a ratio of 2.29:1 for outbound flows (uploads) to inbound flows. Thus, majority of traffic flows experience low throughput amounts. Based on the test results, we design an SDN model and compare its performance to SANReN. The SDN model's results show that it would increase throughput while decreasing delays and page load times. |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/36058 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:52:13.611Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| 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:11427/36058 An analysis of internet traffic flow in SANReN using active and passive measurements Salie, Luqmaan Chavula, Josiah computer science National research and education networks (NRENs) in developing regions such as Africa experience various performance issues due to inadequate infrastructure and resources. The South African National Research Network (SANReN) connects universities, research institutions, and oversees science projects such as the Square Kilometre Array. In this study, we conduct active and passive measurements to assess the performance of SANReN and to identify problem areas in the network. Active measurements were done to determine network performance when accessing SANReN internally (using PerfSONAR) and externally (using Speedchecker). We found that SANReN needs to be reinforced in and around Port Elizabeth, Cape Town, and Durban. Universities in these cities had the highest delays and page load times. We found that the network traffic flowing from PE uses circuitous routes to flow to universities in Johannesburg and Pretoria, causing high delays (medians of 25.26 ms to WITS, 25.47 ms to UJ, and 25.95 ms to UNISA) and high page load times (medians of 237.07 ms to WITS, 272.09 ms to UNISA, 280.47 ms to UJ transferring 31594 bytes of data). Using Cape Town as the traffic source resulted in a low median throughput of 5.47 Gbps for internal active measurements. Throughput from Durban to Cape Town was low as well (4.91 Gbps), causing high page load times between these two cities (medians of 350.32 and 305.22 ms from Durban to UCT and UWC respectively). SANReN's passive measurements results show us that there is a ratio of 11.16:1 for download speed to upload speed. We also observe a ratio of 2.29:1 for outbound flows (uploads) to inbound flows. Thus, majority of traffic flows experience low throughput amounts. Based on the test results, we design an SDN model and compare its performance to SANReN. The SDN model's results show that it would increase throughput while decreasing delays and page load times. 2022-03-14T05:13:02Z 2022-03-14T05:13:02Z 2021 2022-03-14T05:12:34Z Master Thesis Masters MSc http://hdl.handle.net/11427/36058 eng application/pdf Department of Computer Science Faculty of Science |
| spellingShingle | computer science Salie, Luqmaan An analysis of internet traffic flow in SANReN using active and passive measurements |
| thesis_degree_str | Master's |
| title | An analysis of internet traffic flow in SANReN using active and passive measurements |
| title_full | An analysis of internet traffic flow in SANReN using active and passive measurements |
| title_fullStr | An analysis of internet traffic flow in SANReN using active and passive measurements |
| title_full_unstemmed | An analysis of internet traffic flow in SANReN using active and passive measurements |
| title_short | An analysis of internet traffic flow in SANReN using active and passive measurements |
| title_sort | analysis of internet traffic flow in sanren using active and passive measurements |
| topic | computer science |
| url | http://hdl.handle.net/11427/36058 |
| work_keys_str_mv | AT salieluqmaan ananalysisofinternettrafficflowinsanrenusingactiveandpassivemeasurements AT salieluqmaan analysisofinternettrafficflowinsanrenusingactiveandpassivemeasurements |