Network Traffic Prediction Model in a Data-Driven Digital Twin Network Architecture
The proliferation of immersive services, including virtual reality/augmented reality, holographic content, and the metaverse, has led to an increase in the complexity of communication networks, and consequently, the complexity of network management. Recently, digital twin network technology, which a...
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| Veröffentlicht in: | Electronics (Basel) 2023-09, Vol.12 (18), p.3957 |
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| creator | Shin, Hyeju Oh, Seungmin Isah, Abubakar Aliyu, Ibrahim Park, Jaehyung Kim, Jinsul |
| description | The proliferation of immersive services, including virtual reality/augmented reality, holographic content, and the metaverse, has led to an increase in the complexity of communication networks, and consequently, the complexity of network management. Recently, digital twin network technology, which applies digital twin technology to the field of communication networks, has been predicted to be an effective means of managing complex modern networks. In this paper, a digital twin network data pipeline architecture is proposed that demonstrates an integrated structure for flow within the digital twin network and network modeling from a data perspective. In addition, a network traffic modeling technique using data feature extraction techniques is proposed to realize the digital twin network, which requires the use of massive streaming data. The proposed method utilizes the data generated in the OMNeT++ environment and verifies that the learning time is reduced by approximately 25% depending on the feature extraction interval, while the accuracy remains similar. |
| doi_str_mv | 10.3390/electronics12183957 |
| format | Article |
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Recently, digital twin network technology, which applies digital twin technology to the field of communication networks, has been predicted to be an effective means of managing complex modern networks. In this paper, a digital twin network data pipeline architecture is proposed that demonstrates an integrated structure for flow within the digital twin network and network modeling from a data perspective. In addition, a network traffic modeling technique using data feature extraction techniques is proposed to realize the digital twin network, which requires the use of massive streaming data. 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| source | MDPI - Multidisciplinary Digital Publishing Institute; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| subjects | Access control Augmented reality Communication Communication networks Communications networks Communications traffic Complexity Computer architecture Computer simulation Digital twins Digitization Electronic data processing Feature extraction Interfaces Machine learning Methods Modelling Network management systems Pipelining (computers) Prediction models Software Traffic models User services Virtual reality |
| title | Network Traffic Prediction Model in a Data-Driven Digital Twin Network Architecture |
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