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

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

Abstract

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

CLC Number:

TP316.4

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.

Figures/Tables 7

Fig. 1

Table 1

Experimental parameters

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

Table 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

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