A dynamic task offloading and resource allocation method for a mobile edge computing system

Abstract

The invention relates to a dynamic task unloading and resource allocation method of a mobile edge computing system, belonging to the technical field of wireless communication. The method includes the following steps: S1: modeling mobile edge computing system; S2: modeling user equipment task variables; S3: modeling edge server variables; S4: modeling user equipment task buffer queue model; S5: modeling edge server tasks Buffer queue model; S6: Model long-term average power consumption of the system; S7: Model task unloading, resource allocation and task buffer queue constraints; S8: Determine task unloading and resource allocation strategies based on the long-term average power consumption optimization criteria of the system. The present invention comprehensively considers the dynamic characteristics of random arrival tasks of the user equipment, the characteristics of the task buffer queue of the user equipment and the edge server, and jointly optimizes task unloading and resource allocation, thereby realizing the minimization of the long-term average power consumption of the system.

Description

A dynamic task offloading and resource allocation method for a mobile edge computing system technical field The present invention belongs to the technical field of wireless communication, relate to a kind of mobile edge computing system dynamic task unloading and resource allocation method. Background technique With the rapid development of mobile Internet and the popularization of intelligent terminals, augmented reality (Augment Reality, Applications such as AR), Virtual Reality (VR), and mobile HD video streaming are critical to the quality of service Service, QoS) demand is getting higher and higher. However, the lack of processing power of smart user devices and traditional mobile cloud computing The performance limitations of (Mobile Cloud Computing, MCC) technology make it difficult for the network to meet the user's continuous processing in a short period of time. Business needs for handling large amounts of data. In response to this problem, mobile edge ...

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

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