Cooperation system and method based on mobile edge computing

Abstract

A cooperation system and method based on mobile edge computing comprises a device layer and a control layer, the device layer provides computing resource information of a terminal device and a serverand task information of the terminal device for the control layer, and the control layer formulates a task unloading decision according to the received information and returns the task unloading decision to the device layer; according to the method, part of tasks are unloaded to the idle equipment of the user for calculation, so that the load of the MEC server is reduced; the idle equipment of theuser is brought into the edge computing network, so that the resource utilization rate of the edge computing network is improved, and the computing capability of the edge computing network is improved. According to the method, an HAHA optimization algorithm is adopted, unloading decision and resource allocation are jointly optimized by taking energy consumption and calculation cost as targets, and an adaptive mechanism enables different operators to update the selected probability according to historical experience and environmental perception, so that individual evolution has autonomy, the best task unloading decision is made, and the task scheduling efficiency is improved. Therefore, the performance of the edge computing network is improved.

Description

technical field [0001] The invention belongs to the technical field of edge computing network, and in particular relates to a collaboration system and method based on mobile edge computing. Background technique [0002] The fifth generation of mobile communication technology (5G) is booming, giving birth to a large number of new application fields, such as driverless driving, interactive games, and virtual reality. However, in these application fields, especially those that are computationally intensive and time-delay sensitive, powerful computing capabilities of mobile devices are required. However, the computing power of mobile devices is limited, and the problem that its computing resources cannot meet the computing needs of applications is becoming more and more serious. [0003] In the prior art, these computational tasks are offloaded to the cloud for processing, and then the cloud returns the processed results to the device. On the one hand, the distance between the...

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Problems solved by technology

[0007] 1. Only the case of offloading tasks to the MEC server is studied, and the computing resources of the user's idle devices are not fully utilized. Therefore, the utilization rate of edge computing network resources is low, and there is a lot of room for improvement in computing power

[0008] 2. The problem of literature research belongs to black box problem, and the effect of neural network on black box problem optimization is not ideal, the global search ability of the algorithm is weak, and it is easy to fall into local optimum, so the quality of task offloading decision cannot be guaranteed

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