Joint task unloading and resource allocation method in master-slave MEC network

Abstract

The invention relates to a joint task unloading and resource allocation method in a master-slave MEC network, and belongs to the field of wireless communication and mobile edge computing. The method comprises the following steps: a mobile user in a main cell generates a new calculation task, and sends an unloading request to an MEC server deployed in the cell; the MEC server collects calculation unloading request information of all mobile users in the time slot and delivers the calculation unloading request information to the upper SDN controller; the SDN randomly constructs an initial unloading set for the tasks, performs multi-MEC selection on the tasks in the unloading set, allocates computing resources, updates the unloading set, and solves a minimum objective function value; and if the target function value is not less than the target function value of the last iteration or all tasks are unloaded at the moment, an unloading set, a multi-MEC selection matrix and a computing resource allocation set are output. According to the invention, the total cost of task execution of the mobile users in the main cell is reduced, the number of unloaded mobile users is increased, and the problem caused by insufficient computing resources of a single MEC is relieved.

Description

technical field [0001] The invention belongs to the field of wireless communication and mobile edge computing, and relates to a joint task offloading and resource allocation method in a master-slave MEC network. Background technique [0002] With the rapid development of wireless communication technology and cloud computing, the number of mobile devices accessing wireless networks has exploded, and more and more mobile devices interact through the network. As mobile devices become more and more intelligent, the devices will generate a large number of real-time tasks such as face recognition, image processing, and video decoding. Due to the limited size of mobile devices, they have relatively large computing power and energy consumption. limited. Mobile Edge Computing (Mobile Edge Computing, MEC) can efficiently perform tasks for mobile users and reduce user energy consumption by sinking cloud computing capabilities to the mobile terminal side. It is an effective solution to...

AI Technical Summary

Problems solved by technology

[0006] In view of this, the purpose of the present invention is to provide a method for joint task offloading and resource allocation in the master-slave MEC network, to solve the problem of insufficient MEC computing resources in the master cell

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