Detecting and Predicting Models for QoS Optimization in SDN
Recently, deep learning algorithms and software-defined networking technologies enabled traffic management to be more controllable in IP networking and mobile Internet to yield quality services to subscribers. Quality of service (QoS) needs more effort to optimize QoS performance. More specifically,...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Journal of Computer Networks and Communications |
| Online Access: | http://dx.doi.org/10.1155/2024/3073388 |
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| _version_ | 1832549636577951744 |
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| author | Getahun Wassie Jianguo Ding Yihenew Wondie |
| author_facet | Getahun Wassie Jianguo Ding Yihenew Wondie |
| author_sort | Getahun Wassie |
| collection | DOAJ |
| description | Recently, deep learning algorithms and software-defined networking technologies enabled traffic management to be more controllable in IP networking and mobile Internet to yield quality services to subscribers. Quality of service (QoS) needs more effort to optimize QoS performance. More specifically, elephant flow management is a critical task that needs further research since its heavy hit behavior leads to high CPU utilization, packet drops, high latency, packet overflow, and network congestion problems. For this purpose, we focused on elephant flow management since elephant flows are big flows that hinder good service delivery (QoS) on demand. Hence, elephant flow detection and early prediction techniques optimize QoS. In this regard, we employed DNN and CNN deep learning models to detect elephant flows, and the random forest model predicts elephant flows in the SDN. As a result of our experiments, the findings reveal that deep learning algorithms within the Ryu controller significantly outperform in detecting and predicting performance in order to yield good network throughput. This solution proves to be significant for QoS optimization in data centers. |
| format | Article |
| id | doaj-art-254b6c360cf54f83837bd17776671a8b |
| institution | Kabale University |
| issn | 2090-715X |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Computer Networks and Communications |
| spelling | doaj-art-254b6c360cf54f83837bd17776671a8b2025-02-03T06:10:22ZengWileyJournal of Computer Networks and Communications2090-715X2024-01-01202410.1155/2024/3073388Detecting and Predicting Models for QoS Optimization in SDNGetahun Wassie0Jianguo Ding1Yihenew Wondie2IP Networking and Mobile InternetDepartment of Computer ScienceDepartment of Electrical and Computer EngineeringRecently, deep learning algorithms and software-defined networking technologies enabled traffic management to be more controllable in IP networking and mobile Internet to yield quality services to subscribers. Quality of service (QoS) needs more effort to optimize QoS performance. More specifically, elephant flow management is a critical task that needs further research since its heavy hit behavior leads to high CPU utilization, packet drops, high latency, packet overflow, and network congestion problems. For this purpose, we focused on elephant flow management since elephant flows are big flows that hinder good service delivery (QoS) on demand. Hence, elephant flow detection and early prediction techniques optimize QoS. In this regard, we employed DNN and CNN deep learning models to detect elephant flows, and the random forest model predicts elephant flows in the SDN. As a result of our experiments, the findings reveal that deep learning algorithms within the Ryu controller significantly outperform in detecting and predicting performance in order to yield good network throughput. This solution proves to be significant for QoS optimization in data centers.http://dx.doi.org/10.1155/2024/3073388 |
| spellingShingle | Getahun Wassie Jianguo Ding Yihenew Wondie Detecting and Predicting Models for QoS Optimization in SDN Journal of Computer Networks and Communications |
| title | Detecting and Predicting Models for QoS Optimization in SDN |
| title_full | Detecting and Predicting Models for QoS Optimization in SDN |
| title_fullStr | Detecting and Predicting Models for QoS Optimization in SDN |
| title_full_unstemmed | Detecting and Predicting Models for QoS Optimization in SDN |
| title_short | Detecting and Predicting Models for QoS Optimization in SDN |
| title_sort | detecting and predicting models for qos optimization in sdn |
| url | http://dx.doi.org/10.1155/2024/3073388 |
| work_keys_str_mv | AT getahunwassie detectingandpredictingmodelsforqosoptimizationinsdn AT jianguoding detectingandpredictingmodelsforqosoptimizationinsdn AT yihenewwondie detectingandpredictingmodelsforqosoptimizationinsdn |