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Resource pricing and offloading decisions in mobile edge computing based on the Stackelberg game

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Abstract

We propose a new approach for the organic integration of edge cloud offloading decision and Stackelberg game pricing to address the problem that the current Stackelberg games all allocate edge cloud computing resources equally and ignore the difference of different users’ demand for computing resources. Firstly, the Stackelberg game theory is used to establish a model of the optimal amount of data to be offloaded by users and the optimal number of computing resource blocks to be purchased, which converts the multivariate offloading decision problem of users into a univariate optimization problem, simplifies the offloading decision problem of users, and proves the existence of Nash equilibrium. Secondly, the KKT condition is applied to realize the offloading decision of users to purchase the optimal computing resource blocks. The upper and lower bounds of edge cloud pricing are established. Finally, a dynamic programming-based offloading (DPPO) algorithm for edge cloud pricing is proposed to achieve the optimal pricing of edge cloud utility and maximize each user’s own utility. The simulation results show that the proposed method not only achieves the equilibrium of edge cloud utility and user utility, but also has good convergence and scalability. The DPPO algorithm yields better results than with different pricing and offloading strategies.

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Authors and Affiliations

  1. School of Mechanical Engineering and Automation, Shanghai University, Shanghai, China

    Zongyun Liu & Jingqi Fu

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  1. Zongyun Liu
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  2. Jingqi Fu
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Correspondence to Jingqi Fu.

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This work was funded by the National Science Foundation of China under Grant No. 92067106.

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Liu, Z., Fu, J. Resource pricing and offloading decisions in mobile edge computing based on the Stackelberg game. J Supercomput 78, 7805–7824 (2022). https://doi.org/10.1007/s11227-021-04246-w

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