基于烟花算法的基站群协作计算卸载模型研究

doi:10.16182/j.issn1004731x.joss.20-0700

系统仿真学报 ›› 2022, Vol. 34 ›› Issue (2): 354-365.doi: 10.16182/j.issn1004731x.joss.20-0700

基于烟花算法的基站群协作计算卸载模型研究

许斌^1,^2,³(), 闫文卿¹, 韩卓凡¹, 何广深¹, 邓涛¹, 赵云凯¹, 亓晋¹

^1.南京邮电大学, 江苏南京 210003
^2.南京医药股份有限公司, 江苏南京 210012
^3.金陵科技学院数据科学与智慧软件江苏省重点建设实验室, 江苏南京 211169

收稿日期:2020-09-15 修回日期:2021-01-06 出版日期:2022-02-18 发布日期:2022-02-23
作者简介:许斌(1981-)，男，博士，副教授，研究方向为智能优化，服务组合，边缘计算。E-mail：xubin2013@njupt.edu.cn
基金资助:
国家自然科学基金(61802208);中国博士后基金(2019M651923);江苏省博士后科研资助(2019K223);NUPTSF(NY220060);数据科学与智慧软件江苏省重点建设实验室开放基金资助(2020DS301)

Research on Collaborative Computing Offloading Model for Base Station Groups Based on Fireworks Algorithm

Bin Xu^1,^2,³(), Wenqing Yan¹, Zhuofan Han¹, Guangshen He¹, Tao Deng¹, Yunkai Zhao¹, Jin Qi¹

^1.Nanjing University of Posts and Telecommunications, Nanjing 210003, China
^2.Nanjing Pharmaceutical Co. , Ltd. , Nanjing 210012, China
^3.Jiangsu Key Laboratory of Data Science and Smart Software, Jinling Institute of Technology, Nanjing 211169, China

Received:2020-09-15 Revised:2021-01-06 Online:2022-02-18 Published:2022-02-23

摘要/Abstract

摘要：

车联网、AR、AI等计算密集、时延敏感型应用迅速发展，而移动设备因自身计算能力相对不足，执行此类应用任务时会因高时延而严重影响用户体验甚至无法满足用户需求。针对此问题，提出综合考虑时延与成本的多用户、多MEC（mobile edge computing）服务器的基站群协作计算卸载模型。并提出基于凸优化的改进烟花算法（improved fireworks algorithm based on convex optimization， CVX-FWA）来对模型进行求解，对用户任务进行合理的卸载与资源分配。仿真结果表明，提出的计算卸载方案有效降低了任务总时延成本值，实现计算卸载资源的整体优化配置。

关键词: 移动边缘计算, 卸载决策, 资源分配, 烟花算法, 凸优化

Abstract:

Internet of Vehicles (IoV), AR, AI, and other computing-intensive, time-delay-sensitive applications are developing rapidly. However, due to the relatively insufficient computing capacity of mobile devices, such application tasks face serious latency, which seriously affects user experience and even fails to meet the needs of users. To solve this problem, by comprehensively considering delays and costs, we propose a cooperative computing offloading model based on a multi-user and multi-mobile edge computing (multi-MEC) server for base station groups. In addition, an improved fireworks algorithm based on convex optimization (CVX-FWA) is presented to solve the model and perform reasonable offloading and resource allocation for user tasks. The simulations show that the computing offloading scheme proposed effectively reduces the execution delay and cost of all user tasks and realizes the overall optimal allocation of computing offloading resources.

Key words: mobile edge computing, offloading decision, resource allocation, fireworks algorithm, convex optimization

中图分类号:

TP393

许斌, 闫文卿, 韩卓凡, 何广深, 邓涛, 赵云凯, 亓晋. 基于烟花算法的基站群协作计算卸载模型研究[J]. 系统仿真学报, 2022, 34(2): 354-365.

Bin Xu, Wenqing Yan, Zhuofan Han, Guangshen He, Tao Deng, Yunkai Zhao, Jin Qi. Research on Collaborative Computing Offloading Model for Base Station Groups Based on Fireworks Algorithm[J]. Journal of System Simulation, 2022, 34(2): 354-365.

图/表 8

图1

表1

表2

实验参数

实验参数	数值范围
基站MEC服务器计算能力 $A i$ /（GHz/s）	[100,300]
任务所需要的CPU计算量 $C i$ /GHz	[0.1,2]
任务数据大小 $D i$ /kB	[20,600]
信道带宽 $w$ /MHz	20
功率 $P i$ /W	1.3
背景噪声功率N/dBm	-130

表2

图2

图3

图4

图5

图6

参考文献 23

1	Patel M, Naughton B, Chan C, et al. Mobile-edge Computing Introductory Technical White Paper[R]. Mobile-edge Computing (MEC) Industry Initiative, 2014.
2	Yang X, Chen Z, Li K, et al. Communication-Constrained Mobile Edge Computing Systems for Wireless Virtual Reality: Scheduling and Tradeoff[J]. IEEE Access (S2169-3536), 2018(6): 16665-16677.
3	Fan W, Liu Y. Tang B,et al. Terminal Booster: Collaborative Computation Offloading and Data Caching via Smart Base stations[J]. IEEE Wireless Communications Letters (S2162-2345), 2016, 5(6): 612-615.
4	Tong Zhao, Deng Xiaomei, Ye Feng, et al. Adaptive Computation Offloading and Resource Allocation Strategy in a Mobile Edge Computing Environment[J]. Information Sciences (S0020-0255), 2020, 537: 116-131.
5	Xiao Surong,Liu Chubo, Li Kenli, et al. System Delay Optimization For Mobile Edge Computing[J]. Future Generation Computer Systems (S0167-739X),2020,109:17-28.
6	Jian Chengfeng, Chen Jiawei, Zhang Meiyu, et al. Improved Chaotic Bat Swarm Cooperative Scheduling Algorithm for Edge Computing[J]. Journal of Chinese Computer Systems (S1000-1220), 2019, 40(11):2424-2430.
7	Zhao J, Li Q, Gong Y, et al. Computation Offloading and Resource Allocation for Cloud Assisted Mobile Edge Computing in Vehicular Networks[J]. IEEE Transactions on Vehicular Technology (S0018-9545), 2019, 68(8): 7944-7956.
8	Xu Jia, Li Xuejun, Ding Ruimiao, et al. Energy Efficient Multi-resource Computation Offloading Strategy in Mobile Edge Computing[J]. Computer Integrated Manufacturing Systems (S1006-5911), 2019, 25(4):954-961.
9	YANG Lichao, ZHANG Heli, LI Ming, et al. Mobile Edge Computing Empowered Energy Efficient Task Offloading in 5G[J]. IEEE Transactions on Vehicular Technology (S0018-9545), 2018, 67(7): 6398-6409.
10	Chen Weiwei, Wang Dong, Li Keqin. Multi-user Multi-task Computation Offloading in Green Mobile Edge Cloud Computing[J]. IEEE Transactions on Services Computing (S1939-1374), 2019, 12(5): 726-738.
11	LI He, OTA K, DONG Mianxiong. Learning IOT in Edge: Deep Learning for the Internet of Things with Edge Computing[J]. IEEE Network (S0890-8044), 2018, 32(1): 96-101.
12	Kan T Y, Chiang Y, Wei H Y. Task Offloading and Resource Allocation in Mobile-edge Computing System[C]//Wireless and Optical Communication Conference(WOCC), 2018, 27th. IEEE, 2018: 1-4.
13	IMAGANE K, KANAI K, KATTO J, et al. Evaluation and Analysis of System Latency of Edge Computing for Multimedia Data Processing[C]// Kyoto, Japan: 2016 IEEE 5th Global Conference on Consumer Electronics, 2016.
14	WANG P, YAO C, ZHENG Z, et al. Joint Task Assignment, Transmission, and Computing Resource Allocation in Multilayer Mobile Edge Computing Systems[J]. IEEE Internet of Things Journal (S2327-4662), 2018, 6(2): 2872-2884.
15	ZHANG Jing, XIA Weiwei, YAN Feng, et al. Joint Computation Offloading and Resource Allocation Optimization in Heterogeneous Networks with Mobile Edge Computing[J]. IEEE Access (S2169-3536), 2018, 6:19324-19337.
16	Wang K Z, Yang K, Magurawalage C S. Joint Energy Minimization and Resource Allocation in C-Ran With Mobile Cloud[J]. IEEE Transactions on Cloud Computing (S2168-7161), 2018, 6(3): 760-770.
17	Tran T X, Pompili D. Joint Task Offloading and Resource Allocation for Multi Server Mobile Edge Computing Networks[J]. IEEE Transactions on Vehicular Technology (S1939-9359), 2019, 68(1): 856-868.
18	Zhu T X, Shi T, Li J Z, et al. Task Scheduling in Deadline-aware Mobile Edge Computing Systems[J]. IEEE Internet of Things Journal (S2327-4662), 2019, 6(3):4854-4866.
19	Chen L, Zhou S, Xu J. Computation peer Offloading for Energy-constrained Mobile Edge Computing in Small-Cell Networks[J]. IEEE/ACM Transactions on Networking (S1558-2566), 2018, 26(4): 1619-1632.
20	Borst S, Gupta V, Walid A. Distributed Caching Algorithms for Content Distribution Networks[C]//INFOCOM, 2010 Proceedings IEEE. 2010: 1-9.
21	Tawarmalani M, Salinities N V. Global Optimization of Mixed-integer Nonlinear Programs: A Theoretical And Computational Study[J]. Mathematical Programming (S0025-5610), 2004, 99(3): 563-591.
22	Tan Y, Zhu Y. Fireworks Algorithm for Optimization[J]. Advances in Swarm Intelligence, 2010: 355-364.
23	Mao Y, Zhang J, Letaief K B. Dynamic Computation Offloading for Mobile-Edge Computing with Energy Harvesting Devices[J]. IEEE Journal on Selected Areas in Communications (S1558-0008), 2016: 3590-3605.

CVX-FWA	卸载决策问题
个体维度	用户任务数量
个体	单一卸载决策
种群	不同卸载决策的集合
烟花位置	不同用户任务的卸载决策
适应度值	任务时延成本值

[1]	吴学文, 廖婧贤. 云边协同系统中基于博弈论的资源分配与任务卸载方案[J]. 系统仿真学报, 2022, 34(7): 1468-1481.
[2]	程鹏, 张文柱, 谢书翰, 杨子轩. 基于移动边缘计算的车联网任务卸载研究与仿真[J]. 系统仿真学报, 2022, 34(6): 1304-1311.
[3]	梁江涛, 王慧琴. 基于改进蚁群算法的建筑火灾疏散路径规划研究[J]. 系统仿真学报, 2022, 34(5): 1044-1053.
[4]	王凌, 吴楚格, 范文慧. 边缘计算资源分配与任务调度优化综述[J]. 系统仿真学报, 2021, 33(3): 509-520.
[5]	王青云, 焦德忠, 史铄, 彭根燕, 孙俊华, 段雨昕. 改进的蚁群干扰资源分配方法[J]. 系统仿真学报, 2021, 33(12): 2967-2974.
[6]	张巍, 李权, 周宇球, 罗雨航, 奚培锋. 有源配电网两阶段动态应急抗灾运行策略[J]. 系统仿真学报, 2020, 32(5): 936-945.

基于烟花算法的基站群协作计算卸载模型研究

Research on Collaborative Computing Offloading Model for Base Station Groups Based on Fireworks Algorithm

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 8

参考文献 23

相关文章 6

编辑推荐

Metrics

本文评价