Predictive dynamic multi-flow routing (PD-MFR) algorithm towards sixth generation (6G) software-defined networks
We develop a dynamic Quality of Service (QoS) routing algorithm based on network traffic prediction for Sixth Generation (6G) SDNs. First, we formulate a mixed integer optimization model that incorporates the key constraints for Ultra-Reliable Low Latency Communication (URLLC), enhanced Mobile Broad...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-07-01
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| Series: | Journal of Information and Telecommunication |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/24751839.2025.2532222 |
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| _version_ | 1849701287816331264 |
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| author | Buse Pehlivan Volkan Rodoplu Engincan Tunçay Dilara Eraslan |
| author_facet | Buse Pehlivan Volkan Rodoplu Engincan Tunçay Dilara Eraslan |
| author_sort | Buse Pehlivan |
| collection | DOAJ |
| description | We develop a dynamic Quality of Service (QoS) routing algorithm based on network traffic prediction for Sixth Generation (6G) SDNs. First, we formulate a mixed integer optimization model that incorporates the key constraints for Ultra-Reliable Low Latency Communication (URLLC), enhanced Mobile Broadband (eMBB), and massive Machine-Type Communication (mMTC) traffic. Second, we develop our Predictive Dynamic Multi-Flow Routing (PD-MFR) algorithm for QoS flows based on this optimization model. In PD-MFR, first, the network forms predictions of the aggregate eMBB traffic flow generation rates and makes reservations for the flows on the upcoming routing window. Second, delay-tolerant mMTC flows are scheduled to be routed to fill up the residual capacities that remain after the eMBB flow reservations. Third, URLLC flows are routed reactively. We demonstrate the performance of our PD-MFR algorithm when Autoregressive Integrated Moving Average (ARIMA) and Multi-Layer Perceptron (MLP) models are used in forecasting the eMBB flow generation rates. We measure the performance of PD-MFR against the benchmark QoS-Shortest Path Algorithm (QoS-SPA) in which all of the QoS flows are routed reactively and show that PD-MFR outperforms QoS-SPA significantly. This work advances the state of the art in QoS routing algorithms based on network traffic prediction geared towards next-generation SDNs. |
| format | Article |
| id | doaj-art-ff501148fad94c5ea2cbb7f9b642cefa |
| institution | DOAJ |
| issn | 2475-1839 2475-1847 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Journal of Information and Telecommunication |
| spelling | doaj-art-ff501148fad94c5ea2cbb7f9b642cefa2025-08-20T03:17:59ZengTaylor & Francis GroupJournal of Information and Telecommunication2475-18392475-18472025-07-0113310.1080/24751839.2025.2532222Predictive dynamic multi-flow routing (PD-MFR) algorithm towards sixth generation (6G) software-defined networksBuse Pehlivan0Volkan Rodoplu1Engincan Tunçay2Dilara Eraslan3Department of Electrical and Electronics Engineering, Graduate School, Yaşar University, Izmir, TurkeyDepartment of Electrical and Electronics Engineering, Yaşar University, Izmir, TurkeyDepartment of Electrical and Electronics Engineering, Yaşar University, Izmir, TurkeyDepartment of Electrical and Electronics Engineering, Yaşar University, Izmir, TurkeyWe develop a dynamic Quality of Service (QoS) routing algorithm based on network traffic prediction for Sixth Generation (6G) SDNs. First, we formulate a mixed integer optimization model that incorporates the key constraints for Ultra-Reliable Low Latency Communication (URLLC), enhanced Mobile Broadband (eMBB), and massive Machine-Type Communication (mMTC) traffic. Second, we develop our Predictive Dynamic Multi-Flow Routing (PD-MFR) algorithm for QoS flows based on this optimization model. In PD-MFR, first, the network forms predictions of the aggregate eMBB traffic flow generation rates and makes reservations for the flows on the upcoming routing window. Second, delay-tolerant mMTC flows are scheduled to be routed to fill up the residual capacities that remain after the eMBB flow reservations. Third, URLLC flows are routed reactively. We demonstrate the performance of our PD-MFR algorithm when Autoregressive Integrated Moving Average (ARIMA) and Multi-Layer Perceptron (MLP) models are used in forecasting the eMBB flow generation rates. We measure the performance of PD-MFR against the benchmark QoS-Shortest Path Algorithm (QoS-SPA) in which all of the QoS flows are routed reactively and show that PD-MFR outperforms QoS-SPA significantly. This work advances the state of the art in QoS routing algorithms based on network traffic prediction geared towards next-generation SDNs.https://www.tandfonline.com/doi/10.1080/24751839.2025.2532222Quality of Service (QoS)routingpredictive networkSoftware-Defined Network (SDN) |
| spellingShingle | Buse Pehlivan Volkan Rodoplu Engincan Tunçay Dilara Eraslan Predictive dynamic multi-flow routing (PD-MFR) algorithm towards sixth generation (6G) software-defined networks Journal of Information and Telecommunication Quality of Service (QoS) routing predictive network Software-Defined Network (SDN) |
| title | Predictive dynamic multi-flow routing (PD-MFR) algorithm towards sixth generation (6G) software-defined networks |
| title_full | Predictive dynamic multi-flow routing (PD-MFR) algorithm towards sixth generation (6G) software-defined networks |
| title_fullStr | Predictive dynamic multi-flow routing (PD-MFR) algorithm towards sixth generation (6G) software-defined networks |
| title_full_unstemmed | Predictive dynamic multi-flow routing (PD-MFR) algorithm towards sixth generation (6G) software-defined networks |
| title_short | Predictive dynamic multi-flow routing (PD-MFR) algorithm towards sixth generation (6G) software-defined networks |
| title_sort | predictive dynamic multi flow routing pd mfr algorithm towards sixth generation 6g software defined networks |
| topic | Quality of Service (QoS) routing predictive network Software-Defined Network (SDN) |
| url | https://www.tandfonline.com/doi/10.1080/24751839.2025.2532222 |
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