Mobile edge computing assisted vehicle task unloading method

Abstract

The invention relates to a mobile edge computing auxiliary vehicle task unloading method and belongs to the field of vehicle communication, and vehicle tasks can be divided into unloadable tasks and non-unloadable tasks according to attributes of the vehicle tasks. Selecting a local computing task for the task which cannot be unloaded; For an unloadable task, the vehicle utilizes the local computing resources and the MEC computing resources to process the task together, and the purpose of minimizing the task computing cost is achieved. the optimal offload decision depends on a comparison between benefits brought by the task in local processing and benefits brought by offloading to the MEC. In the task calculation process, the data packet queue is dynamically changed, data packets arrive and leave the queue, and meanwhile packet loss is generated due to time delay. Under the condition that the queue is kept stable, the packet loss rate of the task is optimized, the task calculation costcan be reduced, and the vehicle user data transmission experience is improved.

Description

Technical field [0001] The invention belongs to the technical field of vehicle communication and relates to a mobile edge computing assisted vehicle task offloading method. Background technique [0002] In in-vehicle networks, large-scale mobile applications usually complete services with the assistance of on-board units (OBUs). However, the computing power of OBUs is limited. Applications that require a large amount of data are used in vehicles, such as Augmented Reality (Augmented Reality). , AR), virtual reality (virtual reality, VR), ultra-clear video, etc., have relatively high requirements for delay. The task selection generated in the vehicle cannot be calculated locally to meet the low latency requirement. [0003] Relying on the powerful computing power and storage space of the cloud, offloading tasks to the cloud is one of the alternatives. However, due to the capacity limitations and delay fluctuations of the long-distance deployment of the backbone network and the back...

AI Technical Summary

Problems solved by technology

The advantage of this solution is that the cloud can provide powerful computing resources and reduce the computing load of offloading tasks to the MEC. However, the network delay and fluctuations in the transmission of tasks from the MEC server to the cloud server will also affect the reliability of vehicle services and the QoS of users. to influence

In addition, the MEC server and the cloud server can reduce the time delay when MEC offloads some tasks to the cloud server through a wired connection, but it will increase the cost of deployment.

[0007] Although the joint V2V and V2I unloading task scheme takes into account the heterogeneity of resources, there are the following disadvantages in offloading tasks to nearby vehicles for calculation through V2V: the optimal unloading vehicle cannot be dynamically selected; the computing resources of other vehicles are occupied, and other Energy consumption of the vehicle; packet loss during unloading is not considered

Images