A novel reputation incentive mechanism and game theory analysis for service caching in software-defined vehicle edge com
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A novel reputation incentive mechanism and game theory analysis for service caching in software-defined vehicle edge computing Feng Zeng1
· Yaojia Chen1 · Lan Yao2 · Jinsong Wu3
Received: 7 January 2020 / Accepted: 7 August 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
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. Keywords Software-defined vehicular edge computing · Reputation incentive mechanism · Stackelberg game · Service caching
1 Introduction The advancements in wireless technologies and smart vehicles bring us pervasive vehicular applications, such as Augmented Reality (AR) navigation and traffic prediction, that can offer navigation for drivers and greatly reduce Feng Zeng
[email protected] Yaojia Chen yaojia [email protected] Lan Yao [email protected] Jinsong Wu [email protected] 1
School of Computer Science and Engineering, Central South University, Changsha, China
2
College of Mathematics and Econometrics, Hunan University, Changsha, China
3
Department of Computer Science, Universidad de Chile, Santiago, Chile
the traffic accident rate. However, the surge in demand for real-time communications has put higher demands on the performance of advanced vehicular applications, and reducing the latency of these services remains a major challenge [1]. In order to solve this problem, various caching techniques have been developed to improve user Quality of Experience (QoE) in vehicle applications [2]. Due to the long distance between the vehicle and the cloud server, network transmission of a large amount of data may cause long delay of vehicular applications. Mobile Edge Computing (MEC), as a promising new technology, moves computing and caching resources to the edge of the network, so that it can be physically closer to the user than cloud computi
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