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

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

Abstract

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

CLC Number:

TP393

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.

Figures/Tables 8

Fig. 1

Table 1

Table 2

Experimental parameters

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

Table 2

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

References 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]	Xuewen Wu, Jingxian Liao. Game-Based Resource Allocation and Task Offloading Scheme in Collaborative Cloud-Edge Computing System [J]. Journal of System Simulation, 2022, 34(7): 1468-1481.
[2]	Peng Cheng, Wenzhu Zhang, Shuhan Xie, Zixuan Yang. Research and Simulation of Internet of Vehicles Task Offloading Based on Mobile Edge Computing [J]. Journal of System Simulation, 2022, 34(6): 1304-1311.
[3]	Jiangtao Liang, Huiqin Wang. Study on Building Fire Evacuation Path Planning Based on Improved Ant Colony Algorithm [J]. Journal of System Simulation, 2022, 34(5): 1044-1053.
[4]	Xu Gang, Zhang Lei, Yun Yonggang, Du Li. Simulation and Optimization of Support Resource Allocation for Multi-Aircraft Large Fleet re Dispatch [J]. Journal of System Simulation, 2021, 33(4): 892-899.
[5]	Wang Ling, Wu Chuge, Fan Wenhui. A Survey of Edge Computing Resource Allocation and Task Scheduling Optimization [J]. Journal of System Simulation, 2021, 33(3): 509-520.
[6]	Wang Qingyun, Jiao Dezhong, Shi Shuo, Peng Genyan, Sun Junhua, Duan Yuxin. Improved Ant Colony Optimization Algorithm for Jamming Resource Allocation [J]. Journal of System Simulation, 2021, 33(12): 2967-2974.
[7]	Zhang Wei, Li Quan, Zhou Yuqiu, Luo Yuhang, Xi Peifeng. Two-stage Dynamic Emergency and Disaster Resistance Operation Strategy for Active Distribution Network [J]. Journal of System Simulation, 2020, 32(5): 936-945.
[8]	Zhang Xiangbin, Li Yanyan. Research on Simulation of Manufacturing Grid Resource Allocation Based on Scale Free Network [J]. Journal of System Simulation, 2015, 27(2): 246-254.