Internet of vehicles edge computing task unloading method based on hierarchical reinforcement learning

Abstract

The invention belongs to the technical field of Internet of Vehicles edge computing, and particularly relates to an Internet of Vehicles edge computing task unloading method based on hierarchical reinforcement learning. Firstly, a task unloading problem in an edge computing network of the Internet of Vehicles is modeled as an optimization problem with a minimum delay-energy consumption-cost joint loss function as a target, wherein optimization parameters are a task execution sequence, a computing decision, local resource allocation and transmission power control; then, the application with task relevance is expressed in the form of a directed acyclic graph, implicit features in the application are mined through a graph neural network, and meanwhile the discrete continuous mixed action space is processed through a layered reinforcement learning algorithm; and a simulation experiment is carried out by taking the automobile speed adopted in a real environment as a data set, and a result shows that compared with a heuristic algorithm, the method disclosed by the invention can adaptively adjust task unloading and resource allocation strategies under various environmental parameters, so that a system loss function is more effectively reduced.

Description

technical field [0001] The invention belongs to the technical field of edge computing of the Internet of Vehicles, and in particular relates to a task offloading method for edge computing of the Internet of Vehicles based on layered reinforcement learning. Background technique [0002] With the continuous development of the Internet of Vehicles, applications such as assisted driving, augmented reality, and image processing have been gradually deployed in vehicle equipment to improve the driving experience of people in the vehicle [1]. These applications usually have two characteristics: large demand for computing resources and high real-time requirements. However, in-vehicle devices have limited computing resources and limited energy supply, which often cannot meet the demands of these applications [2]. [0003] By introducing cloud servers with powerful computing capabilities, mobile cloud computing (Mobile Cloud Computing, MCC) is regarded as an effective way to solve the...

AI Technical Summary

Problems solved by technology

On the premise of not destroying the subtask execution correlation, how to effectively deal with this type of application is still a big challenge;

[0006] (2) System loss function

[0007] (3) Computational complexity

The task offloading problem in the edge computing of the Internet of Vehicles is often constructed as a nonlinear mixed integer problem, which is NP-hard and cannot be solved in polynomial time [10]

The long solution time is unacceptable for tasks with high real-time requirements, so it is very important to design a task offloading algorithm that does not sacrifice performance and can solve quickly

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