A Game-Based Scheme for Resource Purchasing and Pricing in MEC for Internet of Things

Yajing Leng,Bowen Ma,Ming Wang,Jiwei Huang,Ying Chen

doi:10.1155/2021/1951141

Abstract

Mobile edge computing (MEC) is emerging as a promising paradigm to support the applications of Internet of Things (IoT). The edge servers bring computing resources to the edge of the network, so as to meet the delay requirements of the IoT devices’ service requests. At the same time, the edge servers can gain profit by leasing computing resources to IoT users and realize the allocation of computing resources. How to determine a reasonable resource leasing price for the edge servers and how to determine the number of resource purchased by users with different needs is a challenging problem. In order to solve the problem, this paper proposes a game-based scheme for resource purchasing and pricing aiming at maximizing user utility and server profit. The interaction between users and the edge servers is modeled based on Stackelberg game theory. The properties of incentive compatibility and envy freeness are theoretically proved, and the existence of Stackelberg equilibrium is also proved. A game-based user resource purchasing algorithm called GURP and a game-based server resource pricing algorithm called GSRP are proposed. It is theoretically proven that solutions of the proposed algorithms satisfy the individual rationality property. Finally, simulation experiments are carried out, and the experimental results show that the GURP algorithm and the GSRP algorithm can quickly converge to the optimal solutions. Comparison experiments with the benchmark algorithms are also carried out, and the experimental results show that the GURP algorithm and the GSRP algorithm can maximize user utility and server profit.

Highlights

With the rapid development of Internet of ings (IoT) technology, various IoT devices such as smart phones and vehicles have been connected to the Internet [1, 2]
We study the problem of resource purchasing and resource pricing from the perspective of users and servers and establish both the user utility function and the server profit function. e goal is to optimize both the user utility and the server profit together
It is proved that the properties of incentive compatibility and envy freeness are satisfied. en, we propose a game-based user resource purchasing algorithm (GURP) and a game-based server resource pricing algorithm (GSRP) which can obtain the optimal solution of Stackelberg equilibrium

Summary

Introduction

With the rapid development of Internet of ings (IoT) technology, various IoT devices such as smart phones and vehicles have been connected to the Internet [1, 2]. Service requests generated by IoT devices usually have strict requirements for computing resources and real-time processing [3]. Is is intolerable for IoT services that require high real-time performance To solve this problem, mobile edge computing (MEC) is proposed. With the development of the IoT, a huge number of service requests have been offloaded to edge servers for computing [9]. (ii) We establish a Stackelberg game model to represent the interaction process of resource purchasing and resource pricing between multiple users and the server. En, we propose a game-based user resource purchasing algorithm (GURP) and a game-based server resource pricing algorithm (GSRP) which can obtain the optimal solution of Stackelberg equilibrium.

System Model and Problem Formulation

Game for Purchasing and Pricing Scheme

Algorithm Design

Performance Evaluation

Related Works

Conclusion