MEC computing resource-constrained Stackelberg game computing offloading method

Lingpeng Shi; Shida Lu; Tianbo Feng; XiuMin Zhao; Xiaolu Chen; Haoyang Cui

doi:10.1117/12.2627680

11 March 2022 MEC computing resource-constrained Stackelberg game computing offloading method

Lingpeng Shi, Shida Lu, Tianbo Feng, XiuMin Zhao, Xiaolu Chen, Haoyang Cui

Proceedings Volume 12160, International Conference on Computational Modeling, Simulation, and Data Analysis (CMSDA 2021); 121600P (2022) https://doi.org/10.1117/12.2627680
Event: International Conference on Computational Modeling, Simulation, and Data Analysis (CMSDA 2021), 2021, Sanya, China

Abstract

Mobile Edge Computing (MEC) effectively improves user service quality by sinking the computing center to the edge of the neighboring terminal users. However, insufficient MEC cloud computing resources have created many obstacles to the promotion and application of edge cloud technology. Aiming at the problem of MEC computing offloading and resource allocation under the condition of limited computing resources, this paper builds a Stackelberg master-slave game model to conduct "cloud-side" interactive analysis on MEC revenue and user service quality, and uses differentiated pricing strategies to strengthen the constraints on offloading strategies. Obtain the largest MEC revenue unloading ratio. Finally, the optimal simulated annealing algorithm is used to iterate the optimal allocation strategy of MEC computing resources. Simulations show that the solution in this paper can effectively improve the MEC rate of return on the basis of optimizing the quality of user experience. This method has guiding significance for the study of unloading problems under resource-constrained conditions.

Citation Download Citation

Lingpeng Shi, Shida Lu, Tianbo Feng, XiuMin Zhao, Xiaolu Chen, and Haoyang Cui "MEC computing resource-constrained Stackelberg game computing offloading method", Proc. SPIE 12160, International Conference on Computational Modeling, Simulation, and Data Analysis (CMSDA 2021), 121600P (11 March 2022); https://doi.org/10.1117/12.2627680

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