Computing resource management method and device for 5G ultra-dense network

Abstract

The invention provides a computing resource management method and device for a 5G ultra-dense network. The method comprises the following steps: acquiring a system state of a current time slot, wherein the system state comprises the calculation task data volume of all the user equipment and the current residual calculation resource total amount of the edge server; inputting the system state into a deep reinforcement learning model to obtain a system action, wherein the system action comprises an unloading decision of the user equipment and a computing resource allocation strategy of edge server cooperation; the unloading decision is used for determining the amount of the task data unloaded to the edge server by the user, and the computing resource allocation strategy coordinated by the edge server represents the total amount of the remaining computing resources of the edge server according to the current time slot of the system and the computing resources allocated to the user equipment served by the edge server. The problem of resource management in an ultra-dense network environment in which multiple servers are densely deployed is effectively solved, the task processing delay is minimized, and the user experience quality is further improved.

Description

technical field [0001] This application relates to the field of edge computing resource allocation, in particular to a computing resource management method and device for 5G ultra-dense networks. Background technique [0002] Driven by the rapid development of smart devices and the fifth-generation mobile communication technology 5G (fifith-generation), various complex services and emerging businesses such as the Industrial Internet of Things, unmanned driving, and smart fire protection continue to emerge. In increasingly complex network scenarios, user equipment will continuously generate different types of tasks, resulting in a large amount of data traffic. The resulting mission data needs to be processed in a timely, reliable and efficient manner. Generally speaking, resources such as computing power and battery capacity of the user equipment are very limited. In order to solve the problem of limited user equipment resources, tasks can be offloaded to edge servers for e...

AI Technical Summary

Problems solved by technology

[0003] At present, in the 5G ultra-dense network combined with mobile edge computing, the network environment becomes more complex due to the compactness of various devices and the diversity of system resources

However, the traditional edge server resource allocation scheme only considers the computing power of the current edge server. When the remaining computing resources cannot support the current task computing, the resulting task edge computing delay will not meet the needs of users.

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