Virtual Network Function Dynamic Deployment Algorithm Based on Prediction for 5G Network Slicing

Lun TANG; Yu ZHOU; Youchao YANG; Guofan ZHAO; Qianbin CHEN

doi:10.11999/JEIT180894

Volume 41 Issue 9

Sep. 2019

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Article Navigation > Journal of Electronics & Information Technology > 2019 > 41(9): 2071-2078

Lun TANG, Yu ZHOU, Youchao YANG, Guofan ZHAO, Qianbin CHEN. Virtual Network Function Dynamic Deployment Algorithm Based on Prediction for 5G Network Slicing[J]. Journal of Electronics & Information Technology, 2019, 41(9): 2071-2078. doi: 10.11999/JEIT180894

Citation:

Lun TANG, Yu ZHOU, Youchao YANG, Guofan ZHAO, Qianbin CHEN. Virtual Network Function Dynamic Deployment Algorithm Based on Prediction for 5G Network Slicing[J]. Journal of Electronics & Information Technology, 2019, 41(9): 2071-2078. doi: 10.11999/JEIT180894

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Virtual Network Function Dynamic Deployment Algorithm Based on Prediction for 5G Network Slicing

doi: 10.11999/JEIT180894

1.
School of Communication and Information Engineering, Chongqing University of Post and Telecommunications, Chongqing 400065, China
2.
Key Laboratory of Mobile Communication Technology, Chongqing University of Post and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (61571073)

Received Date: 2018-09-18
Rev Recd Date: 2019-02-20

Available Online: 2019-03-21

Publish Date: 2019-09-10

Abstract

Abstract

In order to solve the unreasonable virtual resource allocation caused by the dynamic change of service request and delay of information feedback in wireless virtualized network, a traffic-aware algorithm which exploits historical Service Function Chaining (SFC) queue information to predict future load state based on Long Short-Term Memory (LSTM) network is proposed. With the prediction results, the Virtual Network Function (VNF) deployment and the corresponding computing resource allocation problems are studied, and a VNFs’ deployment method based on Maximum and Minimum Ant Colony Algorithm (MMACA) is developed. On the premise of satisfying the minimum resource demand for future queue non-overflow, the on-demand allocation method is used to maximize the computing resource utilization. Simulation results show that the prediction model based on LSTM neural network in this paper obtains good prediction results and realizes online monitoring of the network. The Maximum and Minimum Ant Colony Algorithm based VNF deployment method reduces effectively the bit loss rate and the average end-to-end delay caused by overall VNFs’ scheduling at the same time.
- 5G network slicing,
- Resource allocation,
- Traffic-aware,
- Prediction,
- Virtual Network Function (VNF) scheduling

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References(15)

References

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