Game-Based Resource Allocation and Task Offloading Scheme in Collaborative Cloud-Edge Computing System

doi:10.16182/j.issn1004731x.joss.21-0077

Abstract

Abstract:

Considering the delay, energy consumption and computing resource cost, the utility maximization problem in collaborative cloud-edge system is constructed, and divided into three subproblems: computing resource allocation, uplink power allocation and task offloading strategy. A game-based resource allocation and task offloading(GRATO) scheme is proposed to solve those subproblems. The optimal solution of computing resource allocation is obtained by using convex optimization conditions; a low complexity uplink power allocation method is designed to reduce wireless interfere; a game-based distributed task offloading algorithm (GDTOA) is proposed to optimize the task offloading strategy. Simulation results show that the performance of GRATO is better than other schemes on delay and energy consumption, and it can sense the priority of users, resulting in higher utility and lower latency for emergency users..

Key words: edge computing, resource allocation, computation task offloading, game, utility maximization

CLC Number:

TP301.6

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.

Figures/Tables 10

Fig. 1

Table 1

Experimental parameters

参数	数值
系统带宽 $B$ /MHz	20
用户最大上行发射功率 $p m a x$ /dBm	23
背景噪声功率 $σ 2$ /dBm	-100
从BS到云的单位数据传输时延 $v$ / $μ s / b i t - 1$	$1$
能量系数 $κ$	$5 × 10 - 27$
MEC服务器的CPU频率F/GHz	20
用户设备的CPU频率 $f i l$ /GHz	1
MEC服务器的计算资源成本/ $$ / G H z - 1$	$0.015$
云服务器的计算资源成本/ $$ / G H z - 1$	$0.03$

Table 1

Fig. 2

Table 2

Runtime of schemes

方案	$N = 5$	$N = 10$
MCORA	0.096±0.02	0.22±0.07
COSO	0.63±0.2	0.77±0.18
DCORA	0.71±0.18	0.95±0.32
JTORA	0.93±0.2	2.51±0.4
GRATO	3.26±0.37	11.8±2.5
Exhaustive	32.2±3.9	2 543±36

Table 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

References 23

1	Tao Xiaoyi, Ota Kaoru, Dong mianxiong, et al. Performance Guaranteed Computation Offloading for Mobile-edge Cloud Computing[J]. IEEE Wireless Communications Letters (S2162-2337), 2017, 6(6): 774-777.
2	Zhang Guowei, Fei Shen, Zhang Yueyue, et al. Delay Minimized Task Scheduling in Fog-enabled IoT Networks[C]// International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2018, 10:1-6.
3	Hu Yun Chao, Patel Milan, Sabella Dario, et al. Mobile Edge Computing—A key technology towards 5G[R]. ETSI white paper, 2015, 11(11): 1-16.
4	Tran Tuyen X, Hajisami Abolfazl, pandey parul, et al. Collaborative Mobile Edge Computing in 5G Networks: New Paradigms, Scenarios, and Challenges[J]. IEEE Communications Magazine (S0163-6804), 2017, 55(4): 54-61.
5	Lei Yang, Cao Jiannong, Cheng Hui, et al. Multi-user Computation Partitioning for Latency Sensitive Mobile Cloud Applications[J]. IEEE Transactions on Computers(S0018-9340), 2015, 64(8): 2253-2266.
6	Valeria Cardellini, Vittoria De Nitto Personé, Divacerio Valero,et al. A Game-theoretic Approach to Computation Offloading in Mobile Cloud Computing[J]. Math Program (S0025-5610), 2016, 157(2): 421-449.
7	You Changsheng, Huang Kaibin, Hyukjin Chae, et al. Energy-efficient Resource Allocation for Mobile-edge Computation Offloading[J]. IEEE Transactions on Wireless Communications (S1536-1276), 2016, 16(3): 1397-1411.
8	Reza Rahimi M, Venkatasubramanian Nalini, vasilakos Av, et al. Music: Mobility-aware Optimal Service Allocation in Mobile Cloud Computing[C]// IEEE Sixth International Conference on cloud computing. IEEE, 2013: 75-82.
9	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.
10	Huynh Luan NT, Pham Quoc-Viet, Pham Xuan-Qui, et al. Efficient Computation Offloading in Multi-Tier Multi-Access Edge Computing Systems: A Particle Swarm Optimization Approach[J].Applied Sciences (S2076-3417), 2020(10): 203.
11	Lyu X., Tian H, Zhang P, et al. Multi-user Joint Task Offloading and Resources Optimization in Proximate Clouds[J]. IEEE Transactions on Vehicular Technology(S0018-9545), 2017, 66(4): 3435–3447.
12	Tran Tuyen X, Pompili Dario. Joint Task Offloading and Resource Allocation for Multi-server Mobile-edge Computing Networks[J]. IEEE Transactions on Vehicular Technology (S0018-9545), 2019, 68(1): 856-868.
13	Ren Jinke, Yu Guanding, Cai Yunlong, et al. Latency optimization for Resource Allocation in Mobile-edge Computation Offloading[J]. IEEE Transactions on Wireless Communications (S1536-1276), 2018, 17(8): 5506-5519.
14	Gu Yunan, Zheng Chang, Miao Pan, et al. Joint radio and computational resource allocation in IoT fog computing[J].IEEE Transactions on Vehicular Technology (S0018-9545), 2018, 67(8): 7475-7484.
15	Ma S, Guo S, Wang K, et al. A cyclic game for service-oriented resource allocation in edge computing[J]. IEEE Transactions on Services Computing (S1939-1374), 2020, 13(4): 723-734.
16	Chen Yifan, Zhi Y, Yang B, et al. A Stackelberg Game Approach to Multiple Resources Allocation and Pricing in Mobile Edge Computing[J]. Future Generation Computer Systems (S0167-739X), 2020, 108: 273-287.
17	Chen Xu. Decentralized Computation Offloading Game for Mobile Cloud Computing[J]. IEEE Transactions on Parallel and Distributed Systems (S1045-9219), 2014, 26(4): 974-983.
18	Hu Junyan, Li kenli, Liu chubo, et al. Game-Based Task Offloading of Multiple Mobile Devices with QoS in Mobile Edge Computing Systems of Limited Computation Capacity[J].ACM Transactions on Embedded Computing Systems (S1539-9087), 2020, 19(4): 1-21.
19	Long Long, Liu zichen, Zhou Yiqing, et al. Delay Optimized Computation Offloading and Resource Allocation for Mobile Edge Computing[C]// IEEE 90th Vehicular Technology Conference (VTC2019-Fall), IEEE, 2019:1-5.
20	Misra S, Sarkar S. Priorty-based Time-slot Allocation in Wireless Body Area Networks during Medical Emergency Situations: An Evolutionany Game-Thearetic Perspective[J]. IEEE Journal of Biamed and Health Informatios, 2015, 19(2): 541-548.
21	Tammer K.. The Application of Parametric Optimization and Imbedding to the Foundation and Realization of a Generalized Primal Decomposition Approach[J]. Mathematical research (S0138-3019), 1987, 35: 376-386.
22	Zhao Junhui, Li Qiuping, Gong Yi, 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.
23	Ren J, Yu G, He Y, et al. Collaborative Cloud and Edge Computing for Latency Minimization[J]. IEEE Transactions on Vehicular Technology (S0018-9545), 2019, 68(5): 5031-5044.

[1]	Zhou Zicong, Zeng Junjie, Hu Yue, Zhu Zhengqiu, Yin Quanjun. Multi-agent Reinforcement Learning Method for Wargame Simulation Based on Suboptimal Demonstration Guidance [J]. Journal of System Simulation, 2026, 38(5): 1277-1289.
[2]	Li Guozheng, Wang Rui, Fan Shichao, Cai Xintong, Zhai Xinyue. Review on Optimization of Simulation Modeling Strategies for Spacecraft Orbit Avoidance [J]. Journal of System Simulation, 2026, 38(4): 855-868.
[3]	Liu Wei, Chen Delong, Liu Ze, Wang Rui, Li Kaiwen, Zhang Tao. Optimization of Air Defense and Antimissile Firepower Resource Allocation Based on Adaptive Hybrid Evolution [J]. Journal of System Simulation, 2026, 38(4): 959-973.
[4]	Liu Dayong, Dong Zhiming, Guo Qisheng, Gao Ang, Qiu Xuehuan. Construction Approach of LLM-empowered Tactical Wargame Decision-making Agents [J]. Journal of System Simulation, 2026, 38(3): 758-775.
[5]	Wu Shuxia, Zhang Junjie, Chen Delong, Chen Zheyi. Resource-efficient Continuous Learning Framework for Edge Real-time Video Analytics [J]. Journal of System Simulation, 2026, 38(2): 294-306.
[6]	Yan Qiang, Zhang Qianyu, Wei Na. Evolutionary Game-based Analysis of Responses to Hallucinations in Generative Artificial Intelligence [J]. Journal of System Simulation, 2026, 38(2): 399-415.
[7]	Liu Quan, Wang Yu, Liu Linyue, Chen Hao, Huang Jian. Knowledge Closed-loop Driving-based Intelligent Game Confrontation Simulation [J]. Journal of System Simulation, 2026, 38(2): 416-432.
[8]	Guo Rongyu, Li Xiaobin, Jiang Pei, Li Chuanjiang, Liu Shanhui, Ma Jun. Benefit Distribution Optimization Model and Simulation for Multi-mode Operation of Industrial Software Platforms [J]. Journal of System Simulation, 2025, 37(9): 2242-2257.
[9]	Shi Xuanli, Chen Weineng, Song An, Zhao Tianfang. Resource Allocation Method for Virus Spreading Control Based on Multi-granularity Cooperative Coevolution [J]. Journal of System Simulation, 2025, 37(8): 2043-2060.
[10]	Yu Zhongan, Xiao Hongliang, Xia Qiangwei, Liu Jiawei. Simulation Study on Optimizing Microgrid Scheduling with Electric Vehicle Participation Under V2G Mode [J]. Journal of System Simulation, 2025, 37(6): 1412-1426.
[11]	Gu Xueqiang, Luo Junren, Zhou Yanzhong, Zhang Wanpeng. Survey on Large Language Agent Technologies for Intelligent Game Theoretic Decision-making [J]. Journal of System Simulation, 2025, 37(5): 1142-1157.
[12]	Yao Changhua, Bi Shanning, Ma Rufei, Yu Xiaohan, Li Jiaqiang, Chen Jinli. Method for Dynamic Coalition Formation of Wargame Agent for Force Cooperation [J]. Journal of System Simulation, 2025, 37(5): 1188-1196.
[13]	Zhao Huijin, Chen Yu. Modeling and Simulation of Intelligent Underwater Acoustic Countermeasure Based on the Matrix Game [J]. Journal of System Simulation, 2025, 37(5): 1329-1342.
[14]	Wang Rui, Li Xiangyang, Wang Dong, Ma Hongguang, Zhang Zhili. A Radar Countermeasure Modeling Method Incorporating Cognitive Bias [J]. Journal of System Simulation, 2025, 37(4): 1090-1101.
[15]	Su Jiongming, Luo Junren, Chen Shaofei. An Empirical Analysis of New Perspectives for Strategy Solving in Intelligent Game-theoretic Decision-making [J]. Journal of System Simulation, 2025, 37(2): 345-361.