An Adaptive Control Scheme for Data-Driven Traffic Migration Engineering on 5G Network
Adaptive control of traffic engineering (TE) based on 5G network function virtualization (NFV) authorizes the efficient and dynamic network resource allocation, whose utilization is increasingly wide and will become more widespread. In this paper, we first devise an adaptive control scheme for data-...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/14/6/1105 |
| _version_ | 1797482096166436864 |
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| author | Zhaohui Zhang Xiaofei Min Yue Chen |
| author_facet | Zhaohui Zhang Xiaofei Min Yue Chen |
| author_sort | Zhaohui Zhang |
| collection | DOAJ |
| description | Adaptive control of traffic engineering (TE) based on 5G network function virtualization (NFV) authorizes the efficient and dynamic network resource allocation, whose utilization is increasingly wide and will become more widespread. In this paper, we first devise an adaptive control scheme for data-driven traffic migration engineering (TME) on the 5G virtual network. The proposed TME technology focuses on a 5G enhancing mobile broadband (eMBB) network application scenario and takes the network operating expenditure (OPEX) as the main research target. Firstly, we predict the network traffic of the virtual network through the constructed traffic predicted mathematical model. Then, based on the triangle inequality violation (TIV) theorem, some local network traffic is adaptively migrated when the predicted link traffic exceeds the peak rate. Consequently, the migrations of logical links in the virtual network layer are completed. Finally, our experiments show that the proposed protocol can effectively improve the key performance indicators (KPIs) of the reconfigured network, such as throughput, delay and energy consumption. Furthermore, the Fridman and Holm statistical hypothesis tests are also used to analyze the simulation data, which proves that the proposed approximate TME algorithm has statistical significance. |
| first_indexed | 2024-03-09T22:23:24Z |
| format | Article |
| id | doaj.art-b42fd485ac69470ba972b1a9a8d9e33c |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-09T22:23:24Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-b42fd485ac69470ba972b1a9a8d9e33c2023-11-23T19:10:56ZengMDPI AGSymmetry2073-89942022-05-01146110510.3390/sym14061105An Adaptive Control Scheme for Data-Driven Traffic Migration Engineering on 5G NetworkZhaohui Zhang0Xiaofei Min1Yue Chen2School of Mathematics and Statistics, Xidian University, Xi’an 710126, ChinaSchool of Mathematics and Statistics, Xidian University, Xi’an 710126, ChinaSchool of Mathematics and Statistics, Xidian University, Xi’an 710126, ChinaAdaptive control of traffic engineering (TE) based on 5G network function virtualization (NFV) authorizes the efficient and dynamic network resource allocation, whose utilization is increasingly wide and will become more widespread. In this paper, we first devise an adaptive control scheme for data-driven traffic migration engineering (TME) on the 5G virtual network. The proposed TME technology focuses on a 5G enhancing mobile broadband (eMBB) network application scenario and takes the network operating expenditure (OPEX) as the main research target. Firstly, we predict the network traffic of the virtual network through the constructed traffic predicted mathematical model. Then, based on the triangle inequality violation (TIV) theorem, some local network traffic is adaptively migrated when the predicted link traffic exceeds the peak rate. Consequently, the migrations of logical links in the virtual network layer are completed. Finally, our experiments show that the proposed protocol can effectively improve the key performance indicators (KPIs) of the reconfigured network, such as throughput, delay and energy consumption. Furthermore, the Fridman and Holm statistical hypothesis tests are also used to analyze the simulation data, which proves that the proposed approximate TME algorithm has statistical significance.https://www.mdpi.com/2073-8994/14/6/1105traffic engineeringtraffic predictiontraffic migrationkey performance indicatorsstatistical hypothesis tests |
| spellingShingle | Zhaohui Zhang Xiaofei Min Yue Chen An Adaptive Control Scheme for Data-Driven Traffic Migration Engineering on 5G Network Symmetry traffic engineering traffic prediction traffic migration key performance indicators statistical hypothesis tests |
| title | An Adaptive Control Scheme for Data-Driven Traffic Migration Engineering on 5G Network |
| title_full | An Adaptive Control Scheme for Data-Driven Traffic Migration Engineering on 5G Network |
| title_fullStr | An Adaptive Control Scheme for Data-Driven Traffic Migration Engineering on 5G Network |
| title_full_unstemmed | An Adaptive Control Scheme for Data-Driven Traffic Migration Engineering on 5G Network |
| title_short | An Adaptive Control Scheme for Data-Driven Traffic Migration Engineering on 5G Network |
| title_sort | adaptive control scheme for data driven traffic migration engineering on 5g network |
| topic | traffic engineering traffic prediction traffic migration key performance indicators statistical hypothesis tests |
| url | https://www.mdpi.com/2073-8994/14/6/1105 |
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