5G network slicing algorithm based on prediction

Jiageng ZHANG; Min ZHU; Feng DU; Qi WANG; Jun LIU; Zhihai SUO; Li WANG

doi:10.11959/j.issn.1000-0801.2021225

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5G network slicing algorithm based on prediction

Research and Development | 更新时间：2024-06-05

- 5G network slicing algorithm based on prediction
- Telecommunications Science Vol. 37, Issue 9, Pages: 74-85(2021)
- 作者机构：
  
  1. 西安交通大学网络信息中心，陕西西安 710054
  2. 西安交通大学电子与信息学部，陕西西安 710054
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2021225
  CLC： TP3
- Published Online：2021-09，
  
  Published：20 September 2021
- 稿件说明：
移动端阅览
Jiageng ZHANG, Min ZHU, Feng DU, et al. 5G network slicing algorithm based on prediction[J]. Telecommunications science, 2021, 37(9): 74-85.
DOI：

Jiageng ZHANG, Min ZHU, Feng DU, et al. 5G network slicing algorithm based on prediction[J]. Telecommunications science, 2021, 37(9): 74-85. DOI： 10.11959/j.issn.1000-0801.2021225.

摘要

5G网络实时应用场景对网络切片的建立提出了严格的要求，需要使用预测算法提前隔离资源，降低网络切片的建立时间。提出了基于预测的 5G 网络切片算法，以四阶矩为代价函数，在算法复杂度不高的前提下提供必要的预测精度，根据预测结果在5G网络中提前隔离虚拟节点资源和虚拟链路资源，当网络切片请求到达时，直接拉起容器，完成网络切片的动态创建。仿真结果表明，所提算法的预测精度能够达到 90%，在复用原始网络切片资源的条件下，新请求网络切片的创建时间减少50%。

Abstract

5G on line scenario needs short build time of network slice

it can use prediction algorithm to isolate network resource in advance to reduce the construction time of network slice.A 5G network slicing algorithm based on prediction was proposed

which used fourth square as cost function to predict the network resource requests with accepted accurate and lower complexity of algorithm.And then based on the prediction result

virtual nodes and links resource were allocated

and container was pulled up when network slice request arrives to network

the dynamic creation of network slices was completed.The simulation results show that the proposed algorithm can get 90% accurate of prediction

and reduce 50% construction time of network slice

and improve the network utilization ration，when new network slice reusing the original network slice.

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Keywords

references

LIU Y Q , PENG M G , SHOU G C , et al . Toward edge intelligence:multiaccess edge computing for 5G and Internet of things [J ] . IEEE Internet of Things Journal , 2020 , 7 ( 8 ): 6722 - 6747 .

BARAKABITZE A A , BARMAN N , AHMAD A , et al . QoE management of multimedia streaming services in future networks:a tutorial and survey [J ] . IEEE Communications Surveys＆ Tutorials , 2020 , 22 ( 1 ): 526 - 565 .

ZHANG X L , WANG D , YAN K , et al . Design of network slicing system based on SDN/NFV [J ] . Frontiers in Artificial Intelligence and Applications , 2019 , 10 ( 2 ): 969 - 974 .

ZHU M , QU H , ZHAO J H . Instance expansion algorithm for micro-service with prediction [J ] . Electronics Letters , 2018 , 54 ( 6 ): 356 - 357 .

ZHAO J , ZHANG H B , WANG G . Projected kernel recursive maximum correntropy [J ] . IEEE Transactions on Circuits and Systems II:Express Briefs , 2018 , 65 ( 7 ): 963 - 967 .

SHARIATI N , BJÖRNSON E ,, BENGTSSON M , et al . Low-complexity polynomial channel estimation in large-scale MIMO with arbitrary statistics [J ] . IEEE Journal of Selected Topics in Signal Processing , 2014 , 8 ( 5 ): 815 - 830 .

MA W T , DUAN J D , CHEN B D , et al . Recursive generalized maximum correntropy criterion algorithm with sparse penalty constraints for system identification [J ] . Asian Journal of Control , 2017 , 19 ( 3 ): 1164 - 1172 .

TSAO Y , FANG S H , SHIAO Y . Acoustic echo cancellation using a vector-space-based adaptive filtering algorithm [J ] . IEEE Signal Processing Letters , 2015 , 22 ( 3 ): 351 - 355 .

PADHI T , CHANDRA M , KAR A . Adaptive proportionate normalized least mean squares channel equalizer for MIMO-OFDM systems [C ] // 2015 Annual IEEE India Conference (INDICON) . Piscataway:IEEE Press , 2015 : 1 - 4 .

GHASEMI S , KAMIL R , MARHABAN M H . Nonlinear thf-fxlms algorithm for active noise control with loudspeaker nonlinearity [J ] . Asian Journal of Control , 2016 , 18 ( 2 ): 502 - 513 .

SRAZHIDINOV R , KAMIL R , MOHD NOOR S B . NLFXLMS and THF-NLFXLMS algorithms for Wiener-Hammerstein nonlinear active noise control [J ] . Asian Journal of Control , 2017 , 19 ( 5 ): 1791 - 1801 .

EWEDA E . Dependence of the stability of the least mean fourth algorithm on target weights non-stationarity [J ] . IEEE Transactions on Signal Processing , 2014 , 62 ( 7 ): 1634 - 1643 .

EWEDA E , BERSHAD N J , BERMUDEZ J C M . Stochastic analysis of the least mean fourth algorithm for non-stationary white Gaussian inputs [J ] . Signal,Image and Video Processing , 2014 , 8 ( 1 ): 133 - 142 .

GUI G , ADACHI F . Sparse least mean fourth algorithm for adaptive channel estimation in low signal-to-noise ratio region [J ] . International Journal of Communication Systems , 2014 , 27 ( 11 ): 3147 - 3157 .

LI Y S , WANG Y Y , JIANG T . Norm-adaption penalized least mean square/fourth algorithm for sparse channel estimation [J ] . Signal Processing , 2016 , 128 : 243 - 251 .

WANG W Y , ZHAO H Q . Performance analysis of diffusion least mean fourth algorithm over network [J ] . Signal Processing , 2017 , 141 : 32 - 47 .

NI J G , YANG J . Variable step-size diffusion least mean fourth algorithm for distributed estimation [J ] . Signal Processing , 2016 , 122 : 93 - 97 .

YILMAZ Y , KOZAT S S . An extended version of the NLMF algorithm based on proportionate Krylov subspace projections [C ] // 2009 International Conference on Machine Learning and Applications . Piscataway:IEEE Press , 2009 : 404 - 408 .

SAYIN M O , YILMAZ Y , DEMIR A , et al . The Krylov-proportionate normalized least mean fourth approach:formulation and performance analysis [J ] . Signal Processing , 2015 , 109 : 1 - 13 .

BENESTY J , PALEOLOGU C , CIOCHINĂ S , . Proportionate adaptive filters from a basis pursuit perspective [J ] . IEEE Signal Processing Letters , 2010 , 17 ( 12 ): 985 - 988 .

KANG B , YOO J , PARK P . Bias-compensated normalised LMS algorithm with noisy input [J ] . Electronics Letters , 2013 , 49 ( 8 ): 538 - 539 .

JUNG S M , PARK P . Normalised least-mean-square algorithm for adaptive filtering of impulsive measurement noises and noisy inputs [J ] . Electronics Letters , 2013 , 49 ( 20 ): 1270 - 1272 .

ZHI Y F , ZHENG X , LI R . On the convergence behavior of affine projection algorithm with direction error [J ] . Asian Journal of Control , 2014 , 16 ( 2 ): 530 - 538 .

ZHAO H Q , ZHENG Z S . Bias-compensated affine-projection-like algorithms with noisy input [J ] . Electronics Letters , 2016 , 52 ( 9 ): 712 - 714 .

ZHENG Z S , ZHAO H Q . Bias-compensated normalized subband adaptive filter algorithm [J ] . IEEE Signal Processing Letters , 2016 , 23 ( 6 ): 809 - 813 .

ZHENG Z S , LIU Z G , ZHAO H Q . Bias-compensated normalized least-mean fourth algorithm for noisy input [J ] . Circuits,Systems,and Signal Processing , 2017 , 36 ( 9 ): 3864 - 3873 .

YOO J , SHIN J , PARK P . An improved NLMS algorithm in sparse systems against noisy input signals [J ] . IEEE Transactions on Circuits and Systems II:Express Briefs , 2015 , 62 ( 3 ): 271 - 275 .

WANG L , QU H , ZHAO J H , et al . Virtual network embedding with discrete particle swarm optimisation [J ] . Electronics Letters , 2014 , 50 ( 4 ): 285 - 286 .

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