Resource Management Framework Based on the Stackelberg Game in Vehicular Edge Computing

Abstract

With the emergence and development of the Internet of Vehicles (IoV), quick response time and ultralow delay are required. Cloud computing services are unfavorable for reducing delay and response time. Mobile edge computing (MEC) is a promising solution to address this problem. In this paper, we combined MEC and IoV to propose a specific vehicle edge resource management framework, which consists of fog nodes (FNs), data service agents (DSAs), and cars. A dynamic service area partitioning algorithm is designed to balance the load of DSA and improve the quality of service. A resource allocation framework based on the Stackelberg game model is proposed to analyze the pricing problem of FNs and the data resource strategy of DSA with a distributed iteration algorithm. The simulation results show that the proposed framework can ensure the allocation efficiency of FN resources among the cars. The framework achieves the optimal strategy of the participants and subgame perfect Nash equilibrium.

A review of computational offloading research based on game theory in a vehicular edge computing environment

“…Mobile Edge Computing (MEC) and the Internet of Vehicles (IoV) were combined by Li et al 31 . The FOG nodes (FNS), Data Service Agents (DSAS), and CARs make up the proposed specific resource management framework for the vehicle edge.…”

A computing allocation strategy for Internet of things’ resources based on edge computing

“…In the above optimization problem, the objective function (17) is to minimize the sum of weights for completion time of all tasks and the energy consumption of user side, expressed by the total cost C. Constraint (17a) indicates that neither the delay caused by local computing nor the delay caused by migration computing can be greater than the maximum delay that the user can tolerate for task execution. Constraint (17b) indicates that the sum of bandwidth proportions allocated by node j to each task must be less than or equal to 1, that is, the sum of bandwidth occupied by all user tasks migrated to edge node is less than or equal to the maximum bandwidth of edge node.…”

“…[11][12][13][14] Mobile Edge Computing (MEC) can improve network data processing throughput, achieve low-latency and high-reliability data processing services, and solve the dilemma faced by mobile cloud computing. [15][16][17][18] In the MEC environment, in order to better satisfy users' Quality of Service (QoS) requests and improve the quality of user experience, how to efficiently perform the computing and allocation of IoT resources is one of the hot issues that has been widely studied. 19,20 The literature 21 proposed to optimize the energy consumption of edge computing networks under delay constraints.…”