Intelligent Congestion Control of 5G Traffic in SDN using Dual-Spike Neural Network
Software Defined Networking (SDN) with centralized control provides a global view and achieves efficient network resources management. However, using centralized controllers has several limitations related to scalability and performance, especially with the exponential growth of 5G communication. T...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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University of Baghdad
2023-01-01
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| Series: | Journal of Engineering |
| Subjects: | |
| Online Access: | https://joe.uobaghdad.edu.iq/index.php/main/article/view/1657 |
| _version_ | 1797782931474743296 |
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| author | Najwan Sattar Soud Nadia Adnan Shiltagh Al-Jamali |
| author_facet | Najwan Sattar Soud Nadia Adnan Shiltagh Al-Jamali |
| author_sort | Najwan Sattar Soud |
| collection | DOAJ |
| description |
Software Defined Networking (SDN) with centralized control provides a global view and achieves efficient network resources management. However, using centralized controllers has several limitations related to scalability and performance, especially with the exponential growth of 5G communication. This paper proposes a novel traffic scheduling algorithm to avoid congestion in the control plane. The Packet-In messages received from different 5G devices are classified into two classes: critical and non-critical 5G communication by adopting Dual-Spike Neural Networks (DSNN) classifier and implementing it on a Virtualized Network Function (VNF). Dual spikes identify each class to increase the reliability of the classification. Different metrics have been adopted to evaluate the proposed classifier's effectiveness: accuracy, precision, recall, Matthews Correlation Coefficient (MCC), and F1-Score. Compared with a convolutional neural network (CNN), the simulation results confirmed that the DSNN model could enhance traffic classification accuracy by 5%. The efficiency of the priority model also has been demonstrated in terms of Round Trip Time (RTT).
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| first_indexed | 2024-03-13T00:18:55Z |
| format | Article |
| id | doaj.art-f82a4ce42688434bae4b4bfa3005f577 |
| institution | Directory Open Access Journal |
| issn | 1726-4073 2520-3339 |
| language | English |
| last_indexed | 2024-03-13T00:18:55Z |
| publishDate | 2023-01-01 |
| publisher | University of Baghdad |
| record_format | Article |
| series | Journal of Engineering |
| spelling | doaj.art-f82a4ce42688434bae4b4bfa3005f5772023-07-11T18:35:32ZengUniversity of BaghdadJournal of Engineering1726-40732520-33392023-01-0129110.31026/j.eng.2023.01.07Intelligent Congestion Control of 5G Traffic in SDN using Dual-Spike Neural Network Najwan Sattar Soud0Nadia Adnan Shiltagh Al-Jamali 1College of Engineering - University of BaghdadCollege of Engineering - University of Baghdad Software Defined Networking (SDN) with centralized control provides a global view and achieves efficient network resources management. However, using centralized controllers has several limitations related to scalability and performance, especially with the exponential growth of 5G communication. This paper proposes a novel traffic scheduling algorithm to avoid congestion in the control plane. The Packet-In messages received from different 5G devices are classified into two classes: critical and non-critical 5G communication by adopting Dual-Spike Neural Networks (DSNN) classifier and implementing it on a Virtualized Network Function (VNF). Dual spikes identify each class to increase the reliability of the classification. Different metrics have been adopted to evaluate the proposed classifier's effectiveness: accuracy, precision, recall, Matthews Correlation Coefficient (MCC), and F1-Score. Compared with a convolutional neural network (CNN), the simulation results confirmed that the DSNN model could enhance traffic classification accuracy by 5%. The efficiency of the priority model also has been demonstrated in terms of Round Trip Time (RTT). https://joe.uobaghdad.edu.iq/index.php/main/article/view/16575G CommunicationsSoftware Defined NetworkingVirtualized Network FunctionIntelligent spike neural network |
| spellingShingle | Najwan Sattar Soud Nadia Adnan Shiltagh Al-Jamali Intelligent Congestion Control of 5G Traffic in SDN using Dual-Spike Neural Network Journal of Engineering 5G Communications Software Defined Networking Virtualized Network Function Intelligent spike neural network |
| title | Intelligent Congestion Control of 5G Traffic in SDN using Dual-Spike Neural Network |
| title_full | Intelligent Congestion Control of 5G Traffic in SDN using Dual-Spike Neural Network |
| title_fullStr | Intelligent Congestion Control of 5G Traffic in SDN using Dual-Spike Neural Network |
| title_full_unstemmed | Intelligent Congestion Control of 5G Traffic in SDN using Dual-Spike Neural Network |
| title_short | Intelligent Congestion Control of 5G Traffic in SDN using Dual-Spike Neural Network |
| title_sort | intelligent congestion control of 5g traffic in sdn using dual spike neural network |
| topic | 5G Communications Software Defined Networking Virtualized Network Function Intelligent spike neural network |
| url | https://joe.uobaghdad.edu.iq/index.php/main/article/view/1657 |
| work_keys_str_mv | AT najwansattarsoud intelligentcongestioncontrolof5gtrafficinsdnusingdualspikeneuralnetwork AT nadiaadnanshiltaghaljamali intelligentcongestioncontrolof5gtrafficinsdnusingdualspikeneuralnetwork |