Dynamic task unloading and resource allocation method for mobile edge computing system

Abstract

The invention relates to a dynamic task unloading and resource allocation method for a mobile edge computing system, and belongs to the technical field of wireless communication. The method comprisesthe following steps: S1, modeling a mobile edge computing system; S2, modeling a task variable of the user equipment; S3, modeling an edge server variable; S4, modeling a user equipment task buffer queue model; S5, modeling an edge server task buffer queue model; S6, modeling long-term average power consumption of the system; S7, modeling task unloading, resource allocation and task buffer queue constraint conditions; and S8, determining a task unloading and resource allocation strategy based on a system long-term average power consumption optimization criterion. According to the method, the dynamic characteristics of the random arrival task of the user equipment and the task buffer queue characteristics of the user equipment and the edge server are comprehensively considered, and task unloading and resource allocation are jointly optimized, so that the long-term average power consumption minimization of the system is realized.

Description

technical field [0001] The invention belongs to the technical field of wireless communication, and relates to a dynamic task unloading and resource allocation method of a mobile edge computing system. Background technique [0002] With the rapid development of the mobile Internet and the popularization of smart terminals, applications such as augmented reality (Augment Reality, AR), virtual reality (Virtual Reality, VR) and mobile high-definition video streaming have an increasing demand for Quality of Service (QoS). higher. However, the insufficient processing power of smart user equipment and the performance limitations of traditional Mobile Cloud Computing (MCC) technology make it difficult for the network to meet the business needs of users to continuously process large amounts of data in a short period of time. In response to this problem, mobile edge computing technology emerged as the times require. By deploying an edge server at a base station that is close to the s...

AI Technical Summary

Problems solved by technology

At present, literatures have proposed a solution to minimize the long-term average power consumption of user equipment for dynamic task offloading in mobile edge computing systems. In addition, there are few studies that jointly consider the dynamic offloading and resource allocation strategies of user equipment tasks, which makes it difficult for the proposed algorithm to optimize network performance

Images