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

Authors

Chunmao Jiang, 1.College of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China;Follow
Zhenxing Yang, 2.School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150500, China;

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.

Recommended Citation

Jiang, Chunmao and Yang, Zhenxing (2023) "Partial Task Offloading Strategy of Cloud Robots Based on Game Theory under Cloud-Edge Coordination," Journal of System Simulation: Vol. 35: Iss. 5, Article 8.
DOI: 10.16182/j.issn1004731x.joss.22-0014
Available at: https://dc-china-simulation.researchcommons.org/journal/vol35/iss5/8

First Page

987

Revised Date

2022-06-30

DOI Link

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

Last Page

997

CLC

TP316.4

Recommended Citation

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.

DOI

10.16182/j.issn1004731x.joss.22-0014