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A novel reputation incentive mechanism and game theory analysis for service caching in software-defined vehicle edge computing

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Abstract

Service caching can improve the QoS of computationally intensive vehicle applications by pre-storing the necessary application programs and related data for computing tasks on edge servers. In this paper, we propose a new vehicle edge computing framework based on software defined networks, which introduces the reputation to measure the contribution of each vehicle as the basis for providing different quality of services. The process is divided into two phases: in the first phase, the vehicle requests the offload application task from the edge server; and in the second phase, the edge server makes the service caching decision after processing the task. We design the whole interaction process as a kind of incentive mechanism based on reputation via using Stackelberg game modeling, and analyze the optimal strategy for both sides of the game by reverse induction. Furthermore, we also prove the existence and uniqueness of Stackelberg equilibrium in two-stage game, and a genetic optimization algorithm is designed to quickly obtain the optimal strategy for both sides of the game. Experimental results show that the proposed scheme not only brings more profits to the edge server side, but also reduces the average delay by 76 % compared with the ordinary mobile edge computing scheme.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Central South University, Changsha, China

    Feng Zeng & Yaojia Chen

  2. College of Mathematics and Econometrics, Hunan University, Changsha, China

    Lan Yao

  3. Department of Computer Science, Universidad de Chile, Santiago, Chile

    Jinsong Wu

Authors
  1. Feng Zeng
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  2. Yaojia Chen
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  3. Lan Yao
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  4. Jinsong Wu
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Correspondence to Feng Zeng.

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Zeng, F., Chen, Y., Yao, L. et al. A novel reputation incentive mechanism and game theory analysis for service caching in software-defined vehicle edge computing. Peer-to-Peer Netw. Appl. 14, 467–481 (2021). https://doi.org/10.1007/s12083-020-00985-4

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  • DOI: https://doi.org/10.1007/s12083-020-00985-4

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