支持差异化服务的航空信息网络切片策略

doi:10.7527/S1000-6893.2021.25123

电子电气工程与控制

本期目录 | 过刊浏览 | 高级检索

前一篇 | 后一篇

支持差异化服务的航空信息网络切片策略

宋鑫康, 赵尚弘, 王翔

空军工程大学信息与导航学院, 西安 710077

收稿日期:2020-12-18 修回日期:2021-01-07 出版日期:2022-03-15 发布日期:2021-03-01
通讯作者: 宋鑫康 E-mail:afeu_sxk@foxmail.com
基金资助:
国家自然科学基金（91638101）；陕西省自然科学基金（2020 JQ-483）

Aviation information network slicing strategy supporting differentiated services

SONG Xinkang, ZHAO Shanghong, WANG Xiang

College of Information and Navigation, Air Force Engineering University, Xi'an 710077, China

Received:2020-12-18 Revised:2021-01-07 Online:2022-03-15 Published:2021-03-01
Supported by:
National Natural Science Foundation of China (91638101); Shaanxi Natural Science Foundation of China (2020 JQ-483)

摘要/Abstract

摘要： 针对航空集群各作战任务对网络性能需求的差异，研究了灵活耦合任务需求的航空信息网络切片问题。按照任务特性定义网络切片类型及性能特征，以任务资源需求为约束构建切片性能最优化整数规划模型，将网络切片构建问题分解为平台资源聚类和链路资源聚类2个步骤，提出了基于粒子群的平台资源聚类算法和基于感知节点的链路资源聚类算法。平台资源聚类阶段通过基于聚类因子排序的粒子群聚类算法进行迭代求解；链路资源聚类阶段，以各网络切片性能和链路负载率为优化目标，在平台资源聚类的基础上完成链路资源聚类。研究结果表明:所提算法构建的航空信息网络切片可满足不同任务对平台资源以及网络传输能力的需求，平台资源利用率和链路负载率显著改善。

关键词: 航空集群, 网络功能虚拟化, 网络切片, 差异化服务, 资源聚类

Abstract: Considering the difference of network performance requirements of various combat missions in aviation cluster, the problem of aviation information network slicing with flexible coupling mission requirements is studied. According to the task characteristics, the network slice types and performance characteristics are defined, and an integer programming model for slice performance optimization is constructed with task resource demand as the constraint. The network slicing problem is decomposed into two steps:platform resource clustering and link resource clustering. A platform resource clustering algorithm is proposed based on particle swarm optimization, and a link resource clustering algorithm is proposed based on perceptive nodes. In the stage of platform resource clustering, the iterative solution is obtained by using the particle clustering algorithm based on clustering factor sequencing. In the link resource clustering stage, the performance of each network slice and the link load rate are optimized, and link resource clustering is completed on the basis of platform resource clustering. The results show that the aviation information network slice constructed by the proposed algorithm can meet the requirements of different tasks for platform resources and network transmission capacity, significantly improving the utilization rate of platform resources and link load rate.

Key words: aviation cluster, network function virtualization, network slicing, differentiated service, resource clustering

中图分类号:

V249.1

宋鑫康, 赵尚弘, 王翔. 支持差异化服务的航空信息网络切片策略[J]. 航空学报, 2022, 43(3): 325123-325123.

SONG Xinkang, ZHAO Shanghong, WANG Xiang. Aviation information network slicing strategy supporting differentiated services[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022, 43(3): 325123-325123.

参考文献

[1] 王睿, 张克落. 5G网络切片综述[J]. 南京邮电大学学报(自然科学版), 2018, 38(5):19-27. WANG R, ZHANG K L. Survey of 5G networkslicing[J]. Journal of Nanjing University of Posts and Telecommunications (Natural Science Edition), 2018, 38(5):19-27(in Chinese).
[2] 赵尚弘, 陈柯帆, 吕娜, 等. 软件定义航空集群机载战术网络[J]. 通信学报, 2017, 38(8):140-155. ZHAO S H, CHEN K F, LYU N, et al. Software defined airborne tactical network for aeronauticswarm[J]. Journal on Communications, 2017, 38(8):140-155(in Chinese).
[3] CHEN K F,LV N, ZHAO S H, et al. A scheme for improving the communications efficiency between the control plane and data plane of the SDN-enabled airborne tactical network[J]. IEEE Access, 2018, 6:37286-37301.
[4] 梁晓龙, 何吕龙, 张佳强, 等. 航空集群构型控制及其演化方法[J]. 中国科学:技术科学, 2019, 49(3):277-287. LIANG X L, HE L L, ZHANG J Q, et al. Configuration control and evolutionary mechanism of aircraft swarm[J]. Scientia Sinica (Technologica), 2019, 49(3):277-287(in Chinese).
[5] DOLATI M, HASSANPOUR S B, GHADERI M, et al.DeepViNE:Virtual network embedding with deep reinforcement learning[C]//IEEE INFOCOM 2019-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS). Piscataway:IEEE Press, 2019:879-885.
[6] CAO H T, YANG L X, ZHU H B. Novelnode-ranking approach and multiple topology attributes-based embedding algorithm for single-domain virtual network embedding[J]. IEEE Internet of Things Journal, 2018, 5(1):108-120.
[7] ZHANG P Y, YAO H P, LIU Y J. Virtualnetwork embedding based on the degree and clustering coefficient information[J]. IEEE Access, 2016, 4:8572-8580.
[8] BIANCHI F, LO PRESTI F. A Markov reward based resource-latency aware heuristic for the virtual network embedding problem[J]. ACM SIGMETRICS Performance Evaluation Review, 2017, 44(4):57-68.
[9] CHENG X, SU S, ZHANG Z B, et al. Virtual network embedding through topology-aware node ranking[J]. ACM SIGCOMM Computer Communication Review, 2011, 41(2):38-47.
[10] ZHANG P Y, YAO H P, QIU C, et al. Virtual network embedding using node multiple metrics based on simplified ELECTRE method[J]. IEEE Access, 2018,6:37314-37327.
[11] GONG S Q, CHEN J, ZHAO S Y, et al. Virtual network embedding with multi-attribute node ranking based on TOPSIS[J]. KSII Transactions on Internet and Information Systems (TIIS), 2016, 10(2):522-541.
[12] ADABI S, NIA N H, NATEGH M N. A coordinated heuristic approach for virtual network embedding in cloud infrastructure[J]. KSII Transactions on Internet and Information Systems, 2017, 11(5):2346-2361.
[13] CHOWDHURY N MM K, RAHMAN M R, BOUTABA R. Virtual network embedding with coordinated node and link mapping[C]//IEEE INFOCOM 2009. Piscataway:IEEE Press, 2009:783-791.
[14] WANG Y, HU Q, CAO X J. A branch-and-price framework for optimal virtual network embedding[J]. Computer Networks, 2016, 94:318-326.
[15] YANG Z H, GUO Y. Anexact virtual network embedding algorithm based on integer linear programming for virtual network request with location constraint[J]. China Communications, 2016, 13(8):177-183.
[16] WANG G D, ZHAO Y X, HUANG J, et al. An effective approach to controller placement in software defined wide area networks[J]. IEEE Transactions on Network and Service Management, 2018, 15(1):344-355.
[17] LU J, ZHANG Z, HU T, et al. A survey of controller placement problem in software-defined networking[J]. IEEE Access, 2019, 7:24290-24307.
[18] 吕娜, 刘创, 陈柯帆, 等. 一种面向航空集群的集中控制式网络部署方法[J]. 航空学报, 2018, 39(7):321961. LYU N, LIU C, CHEN K F, et al. A method for centralized control network deployment of aeronauticswarm[J]. Acta Aeronautica et Astronautica Sinica, 2018, 39(7):321961(in Chinese).
[19] XIAO L, NAHRSTEDT K. Reliability models and evaluation of internal BGP networks[C]//IEEE INFOCOM 2004. Piscataway:IEEE Press, 2004:1593-1604.
[20] 凌静, 江凌云, 赵迎. 结合模拟退火算法的遗传K-Means聚类方法[J]. 计算机技术与发展, 2019, 29(9):61-65. LING J, JIANG L Y, ZHAO Y. Agenetic K-means clustering method combined with simulated annealing Algorithm[J]. Computer Technology and Development, 2019, 29(9):61-65(in Chinese).
[21] ZHANG L, ZHAO Z H, SHU Y T, et al. Load balancing of multipath source routing in ad hoc networks[C]//2002 IEEE International Conference on Communications. Conference Proceedings. ICC 2002(Cat. No.02CH37333). Piscataway:IEEE Press, 2002:3197-3201.
[22] LEE S J, GERLA M. Split multipath routing with maximally disjoint paths in ad hoc networks[C]//ICC 2001. IEEE International Conference on Communications. Conference Record (Cat. No.01CH37240). Piscataway:IEEE Press, 2001:3201-3205.

E-mail：hkxb@buaa.edu.cn

关于我们

期刊社服务

专业学科

封面文章

友情链接

主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

支持差异化服务的航空信息网络切片策略

Aviation information network slicing strategy supporting differentiated services

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 3

编辑推荐

Metrics

本文评价

[1]	胡利平, 梁晓龙, 何吕龙, 张佳强, 任宝祥, 齐铎. 基于情景分析的航空集群决策规则库构建方法[J]. 航空学报, 2020, 41(S1): 723737-723737.
[2]	吕娜, 陈坤, 陈柯帆, 朱海峰, 潘武. 适应拓扑变化的拥塞最小化网络更新策略[J]. 航空学报, 2020, 41(7): 323661-323661.
[3]	吕娜, 刘创, 陈柯帆, 曹芳波. 一种面向航空集群的集中控制式网络部署方法[J]. 航空学报, 2018, 39(7): 321961-321961.