Vehicle networking task offloading and resource allocation method based on 5G mobile edge computing

Abstract

The invention relates to a method for task offloading and resource allocation of the Internet of Vehicles based on 5G mobile edge computing, and belongs to the technical field of mobile communication. The problem of insufficient computing resources in the network is made up for by adding a backup MEC server, resources are shared between MEC servers to solve the problem of load imbalance, and joint task offloading strategies and computing resource allocation are used to minimize network overhead. Since this optimization problem is difficult to solve directly, the present invention proposes a heuristic algorithm to convert the original problem into local computing resource allocation and task offloading sub-problems to solve respectively. Among them, the sub-problem of local computing resource allocation adopts the method of finding the extreme value of the derivative to obtain the optimal solution. The sub-problem of task unloading is combined with task unloading and MEC resource allocation, and the Lagrange optimization algorithm is used to solve the sub-problem, thereby minimizing network delay and energy consumption. overhead.

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

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