云边协同下基于博弈论的云机器人部分任务卸载策略

doi:10.16182/j.issn1004731x.joss.22-0014

系统仿真学报 ›› 2023, Vol. 35 ›› Issue (5): 987-997.doi: 10.16182/j.issn1004731x.joss.22-0014

云边协同下基于博弈论的云机器人部分任务卸载策略

姜春茂¹(), 杨振兴²

^1.福建工程学院计算机科学与数学学院，福建福州 350118
^2.哈尔滨师范大学计算机科学与信息工程学院，黑龙江哈尔滨 150500

收稿日期:2022-01-08 修回日期:2022-06-30 出版日期:2023-05-30 发布日期:2023-05-22
作者简介:姜春茂(1972-)，男，教授，博士，研究方向为云计算、智能决策等。E-mail：hsdrose@126.com
基金资助:
黑龙江省自然科学基金(LH2020F031)

Partial Task Offloading Strategy of Cloud Robots Based on Game Theory under Cloud-Edge Coordination

Chunmao Jiang¹(), Zhenxing Yang²

^1.College of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China
^2.School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150500, China

Received:2022-01-08 Revised:2022-06-30 Online:2023-05-30 Published:2023-05-22

摘要/Abstract

摘要：

如何合理地利用中心云、边缘云的资源，既降低系统设备能耗，又能缩短任务平均完成时间，是云机器人计算任务卸载面临的重大挑战。将云机器人的计算任务完成时间与能耗作为代价衡量指标，根据自身需求设置不同的代价权重，将多个云机器人的计算任务卸载问题转换成了一种多个玩家参与的博弈模型，设计了一种基于博弈论的部分任务卸载算法(game theory-partial task offloading，GT-PTO)。通过算法下的纳什平衡状态，找到参与者的最佳卸载阈值，从而达到系统总代价的优化。仿真结果表明，采用所提算法进行任务卸载，能够减少云机器人计算任务的能耗，缩短平均任务完成时间，大大提高云边协同服务质量。

关键词: 博弈论, 云机器人, 任务卸载, 云边协同, 能耗优化

Abstract:

How to rationally utilize the resources of central and edge clouds to reduce energy consumption of system equipment and shorten average task completion time is a fundamental challenge for computational task offloading of cloud robots. In this paper, we transform the computational task offloading problem of multiple cloud robots into a multi-actor game model by using the computational task completion time and energy consumption of cloud robots as cost measurement indicators and setting different cost weights according to actual needs. We also develop a game theory-based partial task offloading algorithm (GT-PTO). With the Nash equilibrium state under this algorithm, the optimal offloading threshold for the participants can be found, and the total system cost can be optimized. Simulation results show that the proposed algorithm can be used for task offloading, so as to reduce the energy consumption of computational tasks of cloud robots, shorten the average task completion time, and significantly improve the quality of cloud-edge collaboration services.

Key words: game theory, cloud robot, task offloading, cloud-edge coordination, energy consumption optimization

中图分类号:

TP316.4

姜春茂, 杨振兴. 云边协同下基于博弈论的云机器人部分任务卸载策略[J]. 系统仿真学报, 2023, 35(5): 987-997.

Chunmao Jiang, Zhenxing Yang. Partial Task Offloading Strategy of Cloud Robots Based on Game Theory under Cloud-Edge Coordination[J]. Journal of System Simulation, 2023, 35(5): 987-997.

图/表 7

图1

表1

实验参数

参数

数值

系统带宽B/MHz

上行传输功率 $p i$ /mW

信道噪声功率 $ω 0$ /dBm

能耗系数 $η$

BS到云的单位数据传输时延v/(μs/bit)

MEC服务器计算能力 $F m e c$ /GHz

CCC服务器计算能力 $F c$ /GHz

100

-100

0.5×10^-25

表1

图2

图3

图 4

图5

图6

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云边协同下基于博弈论的云机器人部分任务卸载策略

Partial Task Offloading Strategy of Cloud Robots Based on Game Theory under Cloud-Edge Coordination

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