基于联合优化的网络切片资源分配策略

王再见; 谷慧敏

doi:10.11959/j.issn.1000-436x.2023089

您当前的位置：

首页 >

文章列表页 >

基于联合优化的网络切片资源分配策略

学术通信 | 更新时间：2024-06-06

- 基于联合优化的网络切片资源分配策略
- Network slicing resource allocation strategy based on joint optimization
- 通信学报 2023年44卷第5期页码：234-245
- 作者机构：
  
  1. 安徽师范大学物理与电子信息学院，安徽芜湖 241002
  2. 安徽省智能机器人信息融合与控制工程实验室，安徽芜湖 241002
- 作者简介：
  
  [ "王再见（1980- ），男，安徽定远人，博士，安徽师范大学教授、博士生导师，主要研究方向为面向 5G 的无线多媒体通信、多媒体大数据技术、深度学习、人工智能等" ]
  [ "谷慧敏（1998- ），女，安徽马鞍山人，安徽师范大学硕士生，主要研究方向为无线多媒体通信" ]
- 基金信息：
  
  安徽省自然科学基金资助项目(2008085MF222)
- DOI：10.11959/j.issn.1000-436x.2023089
  中图分类号： TN929.5
- 网络出版日期：2023-05，
  
  纸质出版日期：2023-05-25
- 稿件说明：
移动端阅览
王再见, 谷慧敏. 基于联合优化的网络切片资源分配策略[J]. 通信学报, 2023,44(5):234-245.

Zaijian WANG, Huimin GU. Network slicing resource allocation strategy based on joint optimization[J]. Journal on communications, 2023, 44(5): 234-245.
王再见, 谷慧敏. 基于联合优化的网络切片资源分配策略[J]. 通信学报, 2023,44(5):234-245. DOI： 10.11959/j.issn.1000-436x.2023089.

Zaijian WANG, Huimin GU. Network slicing resource allocation strategy based on joint optimization[J]. Journal on communications, 2023, 44(5): 234-245. DOI： 10.11959/j.issn.1000-436x.2023089.

摘要

为解决5G网络中各类应用差异性大对通信需求侧重点不同造成的网络资源利用率低的问题，提出一种基于联合优化的网络切片资源分配策略，旨在通过综合考虑切片间资源分配和切片内资源调度问题，最大化网络资源利用率和网络收益。首先，在切片间资源分配问题中定义一个切片用户平均满意度函数，基于切片用户数量、切片调度时延以及切片优先级等约束，提出基于用户服务质量（QoS）的比例公平资源分配算法，以权衡各切片之间的公平性和用户需求。其次，在切片内资源调度问题中引入服务降级和资源迁移函数，针对拥塞和非拥塞2 种情况为内部接入用户和外部接入用户分别建立价格模型。基于所提价格模型建立基站与用户之间的Stackelberg博弈，并采用一种低复杂度的全局搜索算法求解该博弈的最佳响应，使基站效用和用户效用最优。仿真结果表明，所提策略能够有效提高资源利用率和网络收益，并降低网络拥塞，较好地实现资源分配的公平性。

Abstract

To improve network resource utilization that was decreased by different applications with different requirements in 5G networks

a network slicing resource allocation strategy based on joint optimization was proposed

which was utilized to maximize both network resource utilization and network revenue by comprehensively considering in tra-slice and inter-slice resource schedule.Firstly

the user’s average satisfaction function was defined in the inter-slicing resource allocation problem.Furthermore

in terms of the number of users

slicing schedule delay and priority

a proportional fair resource allocation algorithm based on quality of service (QoS) was proposed

which was employed to achieve the best tradeoff between fairness and the users’ requirements among slices.Secondly

after two functions (service degradation and resource migration) were introduced in the inter-slice resource schedule problem

two price models were established for internal access users and external access users respectively

where congestion and non-congestion conditions were analyzed.According to the proposed price models

a Stackelberg game between the base station and users was constructed

and a global search algorithm with low complexity was leveraged to obtain the best response of the game

where the best tradeoff between the base station revenue and user utility was obtained.Simulation results show that the proposed strategy can effectively improve resource utilization and network revenue while reducing network congestion.Therefore

it can better realize fairness in resource allocation.

关键词

Keywords

references

CHEN X M , NG D W K , YU W , et al . Massive access for 5G and beyond [J ] . IEEE Journal on Selected Areas in Communications , 2021 , 39 ( 3 ): 615 - 637 .

QI Q , CHEN X M , ZHONG C J , et al . Integrated sensing,computation and communication in B5G cellular Internet of things [J ] . IEEE Transactions on Wireless Communications , 2021 , 20 ( 1 ): 332 - 344 .

CAO T F , XU C Q , DU J P , et al . Reliable and efficient multimedia service optimization for edge computing-based 5G networks:game theoretic approaches [J ] . IEEE Transactions on Network and Service Management , 2020 , 17 ( 3 ): 1610 - 1625 .

伏玉笋 , 杨根科 . 无线超可靠低时延通信:关键设计分析与挑战 [J ] . 通信学报 , 2020 , 41 ( 8 ): 187 - 203 .

FU Y S , YANG G K . Wireless ultra-reliable and low-latency communication:key design analysis and challenge [J ] . Journal on Communications , 2020 , 41 ( 8 ): 187 - 203 .

IMT-2020 (5G) Promotion Group . White paper on 5G concept [S ] . 2015 .

孙国林 , 欧睿杰 , 刘贵松 . 基于深度强化学习的应急物联网切片资源预留算法 [J ] . 通信学报 , 2020 , 41 ( 9 ): 8 - 20 .

SUN G L , OU R J , LIU G S . Deep reinforcement learning-based resource reservation algorithm for emergency Internet-of-things slice [J ] . Journal on Communications , 2020 , 41 ( 9 ): 8 - 20 .

CHIEN H T , LIN Y D , LAI C L , et al . End-to-end slicing with optimized communication and computing resource allocation in multi-tenant 5G systems [J ] . IEEE Transactions on Vehicular Technology , 2020 , 69 ( 2 ): 2079 - 2091 .

FOSSATI F , MORETTI S , PERNY P , et al . Multi-resource allocation for network slicing [J ] . IEEE/ACM Transactions on Networking , 2020 , 28 ( 3 ): 1311 - 1324 .

HAN Y , TAO X F , ZHANG X F , et al . Hierarchical resource allocation in multi-service wireless networks with wireless network virtualization [J ] . IEEE Transactions on Vehicular Technology , 2020 , 69 ( 10 ): 11811 - 11827 .

唐伦 , 魏延南 , 谭颀 , 等 . H-CRAN 网络下联合拥塞控制和资源分配的网络切片动态资源调度策略 [J ] . 电子与信息学报 , 2020 , 42 ( 5 ): 1244 - 1252 .

TANG L , WEI Y N , TAN Q , et al . Joint congestion control and resource allocation dynamic scheduling strategy for network slices in heterogeneous cloud radio access network [J ] . Journal of Electronics ＆Information Technology , 2020 , 42 ( 5 ): 1244 - 1252 .

AJIBARE A T , FALOWO O E . Resource allocation and admission control strategy for 5G networks using slices and users priorities [C ] // Proceedings of 2019 IEEE AFRICON . Piscataway:IEEE Press , 2020 : 1 - 6 .

吴大鹏 , 郑豪 , 崔亚平 . 面向服务的车辆网络切片协调智能体设计 [J ] . 电子与信息学报 , 2020 , 42 ( 8 ): 1910 - 1917 .

WU D P , ZHENG H , CUI Y P . Service-oriented coordination agent design for network slicing in vehicular networks [J ] . Journal of Electronics ＆ Information Technology , 2020 , 42 ( 8 ): 1910 - 1917 .

FOSSATI F , HOTEIT S , MORETTI S , et al . Fair resource allocation in systems with complete information sharing [J ] . IEEE/ACM Transactions on Networking , 2018 , 26 ( 6 ): 2801 - 2814 .

曾菊玲 , 张春雷 , 蒋砺思 , 等 . 基于信道定价的无线虚拟网络资源分配策略:匹配/Stackelberg 分层博弈 [J ] . 电子与信息学报 , 2021 , 43 ( 1 ): 108 - 115 .

ZENG J L , ZHANG C L , JIANG L S , et al . Channel’s price-based resource allocation for wireless virtual network:a hierarchical matching/Stackelberg game approach [J ] . Journal of Electronics ＆ Information Technology , 2021 , 43 ( 1 ): 108 - 115 .

LIETO A , MALANCHINI I , MANDELLI S , et al . Strategic network slicing management in radio access networks [J ] . IEEE Transactions on Mobile Computing , 2022 , 21 ( 4 ): 1434 - 1448 .

TRAN T D , LE L B . Resource allocation for multi-tenant network slicing:a multi-leader multi-follower Stackelberg game approach [J ] . IEEE Transactions on Vehicular Technology , 2020 , 69 ( 8 ): 8886 - 8899 .

RAVEENDRAN N , GU Y N , JIANG C X , et al . Cyclic three-sided matching game inspired wireless network virtualization [J ] . IEEE Transactions on Mobile Computing , 2021 , 20 ( 2 ): 416 - 428 .

GUIJARRO L , VIDAL J R , PLA V . Competition between service providers with strategic resource allocation:application to network slicing [J ] . IEEE Access , 2021 , 9 : 76503 - 76517 .

NHU C N , PARK M . Dynamic network slice scaling assisted by attention-based prediction in 5G core network [J ] . IEEE Access , 2022 , 10 : 72955 - 72972 .

SONG F , LI J , MA C , et al . Dynamic virtual resource allocation for 5G and beyond network slicing [J ] . IEEE Open Journal of Vehicular Technology , 2020 , 1 : 215 - 226 .

SANTOS E , SOUZA R D , REBELATTO J L , et al . Network slicing for URLLC and eMBB with max-matching diversity channel allocation [J ] . IEEE Communications Letters , 2020 , 24 ( 3 ): 658 - 661 .

XIANG H Y , YAN S , PENG M G . A realization of fog-RAN slicing via deep reinforcement learning [J ] . IEEE Transactions on Wireless Communications , 2020 , 19 ( 4 ): 2515 - 2527 .

LI Y J , ZHAO Y L , LI J , et al . Side channel attack-aware resource allocation for URLLC and eMBB slices in 5G RAN [J ] . IEEE Access , 2019 , 8 : 2090 - 2099 .

TANG L , ZHANG Y , LIANG R , et al . Virtual resource allocation algorithm for network utility maximization based on network slicing [J ] . Journal of Electronics ＆ Information Technology , 2017 , 39 ( 8 ): 1812 - 1818 .

CABALLERO P , BANCHS A , VECIANA G D , et al . Network slicing games:enabling customization in multi-tenant networks [J ] . IEEE/ACM Transactions on Networking , 2019 , 27 ( 2 ): 662 - 675 .

浏览量

490

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

智能电网中基于二分图匹配的网络切片资源分配算法

面向B5G多业务场景基于D3QN的双时间尺度网络切片算法

基于耦合相移STAR-RIS的认知无线电系统资源分配算法

基于深度确定性策略梯度的星地融合网络可拆分任务卸载算法

基于图卷积神经网络的超密集物联网资源分配策略