Internet-of-vehicles task unloading and resource allocation method based on 5G mobile edge computing

Abstract

The invention relates to an Internet-of-vehicles task unloading and resource allocation method based on 5G mobile edge computing, and belongs to the technical field of mobile communication. The problem of insufficient computing resources in a network is solved by adding a standby MEC server, the problem of unbalanced load is solved by sharing resources among MEC servers, and a task unloading strategy and computing resource allocation are combined to minimize the network overhead. Due to the fact that the optimization problem is difficult to directly solve, the invention provides a heuristic algorithm, and an original problem is converted into a local computing resource allocation sub-problem and a task unloading sub-problem to be solved respectively. Wherein the local computing resource allocation sub-problem adopts a derivative extremum solving method to obtain an optimal solution, the task unloading sub-problem is combined with task unloading and MEC resource allocation, and a Lagrange optimization algorithm is adopted to solve, so that the network time delay and the energy consumption overhead are minimized.

Description

technical field [0001] The invention belongs to the technical field of mobile communication, and relates to a method for task offloading and resource allocation of Internet of Vehicles based on 5G mobile edge computing. Background technique [0002] The rapid progress of wireless technology and the Internet of Things has accelerated the development of smart cars, enabling smart cars to provide better services for humans. With the emergence of emerging Internet of Vehicles services (such as: driverless, voice processing, 3D navigation, etc.), it is difficult for vehicle terminals to handle such computationally intensive tasks. The surge in task data volume and processing complexity poses challenges to traditional vehicle networks. . At present, many studies are trying to solve the overload application problem of vehicle terminals. Some experts propose to transfer computing tasks to remote cloud servers through cloud-based vehicle networks. Although offloading tasks to remot...

AI Technical Summary

Problems solved by technology

Add a backup MEC server to make up for the lack of computing resources in the network, share resources between MEC servers to solve the problem of load imbalance, and combine task offloading strategies and computing resource allocation to minimize network overhead

A heuristic algorithm is proposed to solve the problem that the optimization objective is difficult to solve directly, and the original problem is converted into local computing resource allocation and task offloading sub-problems to be solved separately

Images