基于粒子群优化算法的5G网络切片功能迁移机制

doi:10.11959/j.issn.2096-109x.2018066

网络与信息安全学报 ›› 2018, Vol. 4 ›› Issue (8): 47-55.doi: 10.11959/j.issn.2096-109x.2018066

基于粒子群优化算法的5G网络切片功能迁移机制

陈强^1,²(),刘彩霞^1,²,李凌书^1,²

¹ 国家数字交换系统工程技术研究中心，河南郑州450002
² 移动互联网安全技术国家工程实验室，北京 100876

修回日期:2018-07-06 出版日期:2018-08-01 发布日期:2018-10-12
作者简介:陈强（1993-），男，辽宁本溪人，国家数字交换系统工程技术研究中心硕士生，主要研究方向为新一代移动通信。|刘彩霞（1974-），女，山东烟台人，国家数字交换系统工程技术研究中心副教授，主要研究方向为移动通信网络、新型网络体系结构。|李凌书（1992-），男，湖北恩施人，国家数字交换系统工程技术研究中心博士生，主要研究方向为新型网络体系结构、网络空间安全。
基金资助:
国家高技术研究发展计划基金资助项目（“863”计划）(2014AA01A701);国家自然科学基金资助项目(61521003);科技部支撑计划基金资助项目(2014BAH30B01)

5G network slicing function migration mechanism based on particle swarm optimization algorithm

Qiang CHEN^1,²(),Caixia LIU^1,²,Lingshu LI^1,²

¹ National Digital Switching System Engineering and Technological R＆D Center,Zhengzhou 450002,China
² National Engineering Laboratory for Mobile Network Security,Beijing 100876,China

Revised:2018-07-06 Online:2018-08-01 Published:2018-10-12
Supported by:
The National High Technology Research and Development Program of China (863 Program)(2014AA01A701);The National Natural Science Foundation of China(61521003);Ministry of Science and Technology Support Plan(2014BAH30B01)

摘要/Abstract

摘要：

在5G的多应用场景中，数据流量经常出现剧增的情况，网络切片中虚拟机资源可能无法满足用户正常需求。鉴于此，提出了一种以负载均衡为目标的网络切片功能迁移机制。该机制基于粒子群优化算法，将虚拟机模拟成粒子，每次迁移过程中，将所有的粒子分成若干个子群，在群内和群间同时应用粒子群优化算法，参照历史最优解和当前全局最优解更新粒子位置，通过选取标记因子较小的粒子实时比较合适度等参数确定最佳目标粒子，完成迁移过程，该机制既提高了收敛速度，又提高了算法精度。通过与其他迁移方法比较，结果表明，所提迁移机制具有精度高、收敛快的优点，并能提升资源的使用效率，降低了数据中心的能耗，具有较好的自适应性。

关键词: 功能迁移, 粒子群算法, 网络切片, 5G

Abstract:

In multi-application scenarios of 5G,data traffic often increases dramatically.Virtual machine resources in network slicing may not meet the normal needs of users.In view of this,a network slicing function migration mechanism aiming at load balancing was proposed.The mechanism simulates the virtual machine into particles based on particle swarm optimization algorithm.In the process of migration,all particles were divided into several subgroups,and particle swarm optimization algorithm was applied within and among groups.According to the historical optimal solution and the current global optimal solution,the particle location was updated,and the best target particles were determined by selecting the smaller particle size of the particle in real time.The mechanism not only improves the convergence speed,but also improves the accuracy of the algorithm.Compared with other migration methods,the results show that the proposed migration mechanism has the advantages of high accuracy and fast convergence.And it can also improve the efficiency of resource utilization,reduce the energy consumption of data center,and has better adaptability.

Key words: function migration, particle swarm optimization algorithm, network slicing, 5G

中图分类号:

TN929.5

陈强,刘彩霞,李凌书. 基于粒子群优化算法的5G网络切片功能迁移机制[J]. 网络与信息安全学报, 2018, 4(8): 47-55.

Qiang CHEN,Caixia LIU,Lingshu LI. 5G network slicing function migration mechanism based on particle swarm optimization algorithm[J]. Chinese Journal of Network and Information Security, 2018, 4(8): 47-55.

图/表 7

图1

图2

图3

表1

图4

图5

图6

参考文献 16

[1]	WANG Q , CHEN J , LIAO G Q . Live migration algorithm of virtual machine for reduce energy consumption[J]. Journal of East China University of Science and Technology, 2018(1): 58-61.
[2]	REN C , MA R T . Network slicing:building customizable 5G network[J]. Journal of East China University of Science and Technology, 2018(1): 26-30.
[3]	SU X , GONG J J , ZENG J . Wireless resources allocation for 5G network slicing[J]. Electronic Engineering ＆ Product World, 2017,24(4): 30-32.
[4]	WEI H , ZHANG Z , FAN B . Network slice access selection scheme in 5G[C]// IEEE Information Technology,Networking,Electronic and Automation Control Conference. 2017: 352-356.
[5]	CHEN W , ZHANG C . Research on virtual machine dynamic migration strategy based on load trend prediction[J]. Journal of Suihua University, 2016(12): 145-149.
[6]	ZHAO C , YAN L , CUI Y H ,et al. Dynamic adjusting threshold algorithm for virtual machine migration[J]. Journal of Computer Applications, 2017.
[7]	LIU J J , CHEN G L , HU C X . Virtual machine migration scheduling strategy based on load characteristic[J]. Computer Engineering, 2011,37(17): 276-278.
[8]	PENG J Y . Research on the energy-efficient based datacenter resource consolidation mechanism[D]. Chengdu:University of Electronic Science and Technology of China, 2013.
[9]	ZHAO S , JI X S , CHENG G Z ,et al. Research on dynamic migration of critical virtual machines for multi-tenancy[J]. Chinese Journal of Network and Information Security, 2017,3(8): 8-17.
[10]	SHI Y , ZHANG H G , WU F S . A method of constructing the model of trusted virtual machine migration[J]. Journal of Computer Research and Development, 2017,54(10): 2284-2295.
[11]	TIWARI P K , JOSHI S . A review on load balancing of virtual machine resources in cloud computing[C]// International Conference on Information and Communication Technology for Intelligent Systems. 2016: 369-378.
[12]	LIU L , YU H F , ZHENG S P . Comprehensive stable VM migration techniques for managing time-varied workload[J]. Application Research of Computers, 2016,33(2): 534-539.
[13]	WANG H , CHEN S , AI M ,et al. Mobility driven network slicing:an enabler of on demand mobility management for 5G[J]. The Journal of China Universities of Posts and Telecommunications, 2017,24(4): 16-26.
[14]	YANG J , ZHANG H J , ZHAO S N ,et al. Virtual machine deployment strategy based on particle swarm optimization algorithm[J]. Journal of Computer Applications, 2016,36(1): 117-121.
[15]	AMENDOLA D , CORDESCHI N , BACCARELLI E . Bandwidth management VMs live migration in wireless fog computing for 5G networks[C]// IEEE International Conference on Cloud Networking. 2016.
[16]	ROST P , BERBERANA I , MAEDER A ,et al. Benefits and challenges of virtualization in 5G radio access networks[J]. IEEE Communications Magazine, 2015,53(12): 75-82.

虚拟机	CPU/核	内存/GB	带宽/(Mbit·s^?1)
A1	4	2	20
A2	2	4	30
A3	1	2	50

基于粒子群优化算法的5G网络切片功能迁移机制

5G network slicing function migration mechanism based on particle swarm optimization algorithm

在线阅读

pdf下载

可视化

摘要/Abstract

引用本文

使用本文

图/表 7

参考文献 16

相关文章 7

Metrics

推荐阅读 0

[1]	徐子钧, 刘建伟, 李耕. 面向5G mMTC的网络切片安全研究[J]. 网络与信息安全学报, 2022, 8(1): 95-105.
[2]	乔康,游伟,王领伟,汤红波. 基于区块链的5G物联网数据共享方案[J]. 网络与信息安全学报, 2020, 6(4): 45-55.
[3]	竺智荣,吕佳玉,叶帼华,姚志强. 轻量级门限群签名方案的改进[J]. 网络与信息安全学报, 2020, 6(4): 120-129.
[4]	潘启润,黄开枝,游伟. 基于隔离等级的网络切片部署方法[J]. 网络与信息安全学报, 2020, 6(2): 96-105.
[5]	陈强,刘彩霞,李凌书. 基于改进式贪婪算法的5G网络切片动态资源调度策略[J]. 网络与信息安全学报, 2018, 4(7): 60-68.
[6]	孙志勇,季新生,游伟,袁泉. 基于安全分级的网络切片备份与重映射方法研究[J]. 网络与信息安全学报, 2018, 4(11): 49-57.
[7]	王良民,刘晓龙,李春晓,杨睛,杨卫东. 5G车联网展望[J]. 网络与信息安全学报, 2016, 2(6): 1-12.