一种计及风险偏好的计算卸载激励远期合同

张碧玲; 焦正阳; 刘家华; 郭彩丽

doi:10.11999/JEIT230617

一种计及风险偏好的计算卸载激励远期合同

doi: 10.11999/JEIT230617

北京邮电大学信息与通信工程学院北京 100876

基金项目: 国家自然科学基金面上项目(62171060)

详细信息

作者简介:
张碧玲：女，教授，博士生导师。研究方向为无线通信网络，物联网，能源互联网，合同理论及其应用

焦正阳：男，硕士生，研究方向为能源互联网、合同理论、信息系统及其应用

刘家华：男，硕士生，研究方向为能源互联网、合同理论、信息系统及其应用

郭彩丽：女，教授，博士生导师。研究方向为无线移动通信技术等

通讯作者:
张碧玲　bilingzhang@bupt.edu.cn

中图分类号: TN919
计量
- 文章访问数: 199
- HTML全文浏览量: 60
- PDF下载量: 24
- 被引次数: 0
出版历程
- 收稿日期: 2023-06-21
- 修回日期: 2024-04-28
- 网络出版日期: 2024-05-15
- 刊出日期: 2024-06-30

A Computational Offloading Incentive Forward Contract Taking into Account Risk Appetite

School of Information and Communication Engineering, Beijing University of Post and Telecommunication, Beijing 100876, China

Funds: The National Natural Science Foundation of China (62171060)

摘要

摘要: 边缘计算网络中，为了激励边缘计算节点(ECNs)参与计算卸载，以缓解计算服务供应商(SP)的计算压力，研究面向远期交易的激励机制。考虑到SP与ECNs之间的信息不对称，且ECN闲置计算资源的不确定性易导致合作风险，基于合同理论，该文提出一种计及风险偏好的计算卸载远期合同激励机制。首先，建立节点风险偏好模型；接着，定义个人理性(IR)约束和激励相容(IC)约束，将激励问题建模为最大化SP收益的远期合同设计问题；最后，化简约束并求解最优远期合同。仿真结果验证了所设计的远期合同的可行性和合理性，并证明该合同能有效激励ECNs参与计算卸载，提升了SP的收益。
- 计算卸载 /
- 合同理论 /
- 远期合同 /
- 风险偏好
Abstract: In edge computing networks, to stimulate the Edge Computing Nodes (ECNs) to assist in computation offloading to relieve the pressure of computing Service Provider (SP), a forward transaction oriented incentive mechanism is studied. Considering that there is information asymmetry between SP and ECNs and the uncertainty of ECNs idle resources can lead to cooperation risks, a risk-aware forward incentive mechanism based on contract theory for computation offloading is proposed. Firstly, a risk preference model for nodes is established; and then the Individual Rationality (IR) constraints and Incentive Compatibility (IC) constraints are defined, and the incentive problem is modeled as a forward contract design problem to maximize the benefits of SP; finally, the optimal forward contract is derived after constraint simplification. The simulation results verify the feasibility and rationality of the proposed forward contract, and prove that the contract can effectively incentivize ECNs to participate in computation offloading and increase the profits of SP.
- Computation offloading /
- Contract theory /
- Forward contract /
- Risk preference

HTML全文

图 1 计算卸载的远期交易场景

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图 2 远期合同的可行性

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图 3 远期合同的单调性

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图 4 不同激励机制下SP的期望收益

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表 1 基本仿真参数设置^[18]

仿真参数	参数值	仿真参数	参数值
ECNs的个数$ {N_{\text{c}}} $	60	ECNs类型个数$ K $	12
设备占用权重因子$ {\mu ^{\text{t}}} $	0.05	单位数据的计算量$ g $ (cycle/Byte)	5
能耗权重因子$ {\mu ^{\text{e}}} $	0.0003	开关电容效率因子$ \kappa $	$ 1.2 \times {10^{ - 11}} $
惩罚因子$ a $	0.01	SP任务收益 $ h $ (Byte)	0.7
风险偏好$ {r_k} $	$ \begin{aligned}{r_k} = \,& 0.4 + \\& \dfrac{{0.9 + 0.4 \times k}}{{12}}\end{aligned}$	收益因子$ \varepsilon $	10

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参考文献(18)

[1]	CHEN Jie, ZHANG Lin, LIANG Yingchang, et al. Resource allocation for wireless-powered IoT networks with short packet communication[J]. IEEE Transactions on Wireless Communications, 2019, 18(2): 1447–1461. doi: 10.1109/TWC.2019.2893335.
[2]	ZHOU Zhenyu, GAO Caixia, XU Chen, et al. Social big-data-based content dissemination in internet of vehicles[J]. IEEE Transactions on Industrial Informatics, 2018, 14(2): 768–777. doi: 10.1109/TII.2017.2733001.
[3]	SU Chunxia, Ye Fang, LIU Tingting, et al. Computation offloading in hierarchical multi-access edge computing based on contract theory and Bayesian matching game[J]. IEEE Transactions on Vehicular Technology, 2020, 69(11): 13686–13701. doi: 10.1109/TVT.2020.3022766.
[4]	YANG Chao, LOU Wei, LIU Yi, et al. Resource allocation for edge computing-based vehicle platoon on freeway: a contract-optimization approach[J]. IEEE Transactions on Vehicular Technology, 2020, 69(12): 15988–16000. doi: 10.1109/TVT.2020.3039851.
[5]	ZHAO Junhui, KONG Ming, LI Qiuping, et al. Contract-based computing resource management via deep reinforcement learning in vehicular fog computing[J]. IEEE Access, 2020, 8: 3319–3329. doi: 10.1109/ACCESS.2019.2963051.
[6]	HUANG Xumin, YU Rong, YE Dongdong, et al. Efficient workload allocation and user-centric utility maximization for task scheduling in collaborative vehicular edge computing[J]. IEEE Transactions on Vehicular Technology, 2021, 70(4): 3773–3787. doi: 10.1109/TVT.2021.3064426.
[7]	DRESSLER F, HANDLE P, and SOMMER C. Towards a vehicular cloud-using parked vehicles as a temporary network and storage infrastructure[C]. The 2014 ACM International Workshop on Wireless and Mobile Technologies for Smart Cities, Philadelphia, USA, 2014: 11–18. doi: 10.1145/2633661.2633671.
[8]	KIM N, KIM D, LEE J, et al. Incentive-based coded distributed computing management for latency reduction in IoT services—a game theoretic approach[J]. IEEE Internet of Things Journal, 2021, 8(10): 8259–8278. doi: 10.1109/JIOT.2020.3045277.
[9]	LIU Yang, XU Changqiao, ZHAN Yufeng, et al. Incentive mechanism for computation offloading using edge computing: a stackelberg game approach[J]. Computer Networks, 2017, 129: 399–409. doi: 10.1016/j.comnet.2017.03.015.
[10]	ALGARVIO H. Risk-sharing contracts and risk management of bilateral contracting in electricity markets[J]. International Journal of Electrical Power & Energy Systems, 2023, 144: 108579. doi: 10.1016/j.ijepes.2022.108579.
[11]	AN Xuming, FAN Rongfei, HU Han, et al. Joint task offloading and resource allocation for IoT edge computing with sequential task dependency[J]. IEEE Internet of Things Journal, 2022, 9(17): 16546–16561. doi: 10.1109/JIOT.2022.3150976.
[12]	LI Yuwei, YANG Bo, WU Hao, et al. Joint offloading decision and resource allocation for vehicular fog-edge computing networks: A contract-stackelberg approach[J]. IEEE Internet of Things Journal, 2022, 9(17): 15969–15982. doi: 10.1109/JIOT.2022.3150955.
[13]	EDERINGTON L H. The hedging performance of the new futures markets[J]. The Journal of Finance, 1979, 34(1): 157–170. doi: 10.2307/2327150.
[14]	ZHANG Biling, LIU Jiahua, WANG Min, et al. Long-term contracts with dynamic asymmetric information for traffic offloading in heterogeneous 5G and beyond networks[J]. IEEE Internet of Things Journal, 2023, 10(4): 3440–3452. doi: 10.1109/JIOT.2022.3225237.
[15]	LIWANG Minghui, GAO Zhibin, and WANG Xianbin. Let’s trade in the future! A futures-enabled fast resource trading mechanism in edge computing-assisted UAV networks[J]. IEEE Journal on Selected Areas in Communications, 2021, 39(11): 3252–3270. doi: 10.1109/JSAC.2021.3088657.
[16]	BOLTON P and DEWATRIPONT M. Contract Theory[M]. Cambridge: The MIT Press, 2004: 77–81.
[17]	JEMBER A G, XU Wenhe, PAN Chao, et al. Game and contract theory-based energy transaction management for internet of electric vehicle[J]. IEEE Access, 2020, 8: 203478–203487. doi: 10.1109/ACCESS.2020.3036415.
[18]	LI Zhenjiang, CHEN Cheng, and WANG Kai. Cloud computing for agent-based urban transportation systems[J]. IEEE Intelligent Systems, 2011, 26(1): 73–79. doi: 10.1109/MIS.2011.10.