Task Offload Optimization Management of Networked Vehicles in Edge Computing Environment

With the continuous development of 5G technology and intelligent technology, intelligent networked vehicles can rely on vehicle-road collaboration technology to form a more specific understanding of traffic situations through edge caching and edge computing services of roadside infrastructure to ensure driving safety. Improve traffic efficiency. The main purpose of this paper is to study the task offloading optimization management of connected vehicles based on edge computing. While constructing the model for offloading computing tasks, this paper uses python programming simulation to implement. Python language has a powerful standard library, and it also provides a large number of application modules, which users can directly call according to their own needs. application. Python language can be mature and widely used in website data crawling, data operation, image system, and Web system development. In this paper, a computing offloading process is divided into two parts, namely code migration and data offloading. The code only contains the general computing code file of the task (that is, the OpenCL kernel program file) and the AI computing code file (that is, the TensorFlow model file).) and a task configuration script file with the input data to be processed by the task. Moreover, the runtime environment of application tasks is pre-arranged on each device, regardless of online deployment or offloading, which greatly reduces the amount of transmitted data.