Task unloading method in mobile edge computing environment and application thereof

Abstract

The invention belongs to the field of edge computing, and particularly relates to a task unloading method in a mobile edge computing environment and application thereof. The method comprises the following steps that S1, a complete work task is divided into different links, and then a workflow dependency graph WDG representing the work task is constructed; s2, determining an attribute set of each task node in the WDG; s3, creating an energy consumption transfer total graph ETG representing the work task; s4, deleting in-degree edges and out-degree edges corresponding to the task nodes which cannot be unloaded, and generating a simplified energy consumption transfer graph ETG1; s5, generating a corresponding linear energy consumption transfer graph ETG2 according to the ETG1; and S6, obtaining a path with the minimum equipment energy consumption by taking the time constraint meeting and the minimum equipment energy consumption of the terminal as an optimization target, and performing task unloading. According to the method, the problems that the existing MEC environment cannot complete calculation task unloading on a complex nonlinear task, and the energy consumption in the task processing process is easy to increase and the real-time performance is easy to reduce are solved.

Description

technical field [0001] The invention belongs to the field of edge computing, and in particular relates to a task offloading method in a mobile edge computing environment and an application thereof. Background technique [0002] With the popularity of online shopping methods such as the Internet, the market size of logistics transportation is also expanding, and logistics companies need to complete the delivery of orders within the specified time. In the overall distribution process, the process of delivering goods from the site to the user is called the "last mile" link. Because the last mile delivery link requires a lot of labor costs, it accounts for 30% and 50% of the total transportation time and total delivery cost. [0003] At present, many companies are developing the technology of using UAVs for goods delivery; UAVs have unique advantages in the last mile delivery scenario because of their flexible deployment, low cost, and high-speed flight characteristics. In the...

AI Technical Summary

Problems solved by technology

[0008] In order to solve the problem that the existing MEC environment cannot complete the calculation task offloading of complex nonlinear tasks, and it is easy to cause the energy consumption of the task processing process to increase and the real-time performance to decrease; the present invention provides a task offloading in the mobile edge computing environment Method and its application

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