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Hai-Sheng Tan, Guo-Peng Li, Zi-Yu Shen, Zi-He Wang, Zhen-Hua Han, Ming-Jun Xiao, Xiang-Yang Li, Guo-Liang Chen. Edge-Centric Pricing Mechanisms with Selfish Heterogeneous Users[J]. Journal of Computer Science and Technology. DOI: 10.1007/s11390-024-3298-y
Citation: Hai-Sheng Tan, Guo-Peng Li, Zi-Yu Shen, Zi-He Wang, Zhen-Hua Han, Ming-Jun Xiao, Xiang-Yang Li, Guo-Liang Chen. Edge-Centric Pricing Mechanisms with Selfish Heterogeneous Users[J]. Journal of Computer Science and Technology. DOI: 10.1007/s11390-024-3298-y
shu

Edge-Centric Pricing Mechanisms with Selfish Heterogeneous Users

  • 1.

    School of Computer Science and Technology, University of Science and Technology of China, Hefei 230026, China

  • 2.

    USTC-Deqing Alpha Innovation Research Institute, Huzhou 313200, China

  • 3.

    Gaoling School of Artificial Intelligence, Renmin University of China, Beijing 100872, China

  • 4.

    Microsoft Research Asia, Shanghai 200232, China

Funds: This work was supported in part by the National Key R&D Program of China under Grant No. 2021ZD0110400, the Key Program of the National Natural Science Foundation of China under Grant No. 62132009 and the Fundamental Research Funds for the Central Universities at China.
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  • Received Date: April 13, 2023
  • Revised Date: January 09, 2024
  • Accepted Date: January 17, 2024
    Abstract
  • Through deploying computing resources close to users, edge computing is regarded as a promising complement to cloud computing to provide low-latency computational services. Meanwhile, edge platforms also play the role of competitors of the cloud platforms in a non-cooperative game, which sets prices for computational resources to attract users with different real-time requirements. In this paper, we propose the edge pricing game under competition (EPGC) and investigate the truthful pricing mechanisms of the edge platform with the objective of maximizing its revenue under three different settings. When all user information is available, the optimal mechanism (OM) can be achieved based on a knapsack problem oracle. With partial information, where users' resource demand is given but their preference information to the edge platform is private, we propose a random sampling mechanism (RSM) that achieves a constant approximation with probability approaching one. We also propose an efficient heuristic greedy mechanism, and we call it GM. Both mechanisms are truthful, GM is directly applicable, while RSM requires minor modifications (RSM+) for deployment in the prior-free setting where all user information is private. Finally, extensive simulations are conducted on the Google cluster dataset. The results validate our theoretical analysis that RSM+ works well in the market where edge resources are scarce, while GM performs better when the edge platform has a larger capacity constraint.
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