Cooperative computing unloading method based on splittable task in vehicle-mounted edge computing environment

Abstract

The invention provides a cooperative computing unloading method based on a splittable task in a vehicle-mounted edge computing environment, and the method comprises the steps: sending a to-be-processed task unloading request when the calculation amount of a to-be-processed task cannot be borne; acquiring relevant information of callable computing resources according to feedback of the to-be-processed task unloading request; and obtaining an optimal splitting ratio for task segmentation according to the related information of the callable computing resources. Therefore, the optimal unloading decision is selected by comprehensively considering the computing power of the vehicle, and the minimum task completion time delay can be conveniently obtained. After the request vehicle receives the relevant information of the callable computing resources, a task splitting model is constructed to perform construction, the optimal splitting ratio is obtained, a distributed task splitting and unloading scheme is practiced, it is guaranteed that each request vehicle autonomously decides splitting and unloading decisions of a task only based on received information, and frequent information interaction in a centralized scheme is avoided.

Description

technical field

[0001] The present invention relates to the technical field of mobile edge computing Internet of Vehicles, in particular to a splittable task-based collaborative computing offloading method in a vehicle edge computing environment. Background technique

[0002] To cope with the explosive growth of MEC data, a mobile cloud computing (MCC)-based vehicular cloud computing network (VCC) has been envisioned as a potential solution. Vehicles in MCC offload complex computing tasks that are difficult to complete within the maximum tolerance time to remote cloud data centers for execution through high-speed and highly reliable air interfaces.

[0003] Moreover, the transmission distance between the vehicle and the remote cloud data center is too long. Considering the rapidly changing wireless channel and the frequent data exchange between a large number of mobile users and the mobile cloud, the backhaul network will become congested, which will reduce the unloading eff...

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