Delay and energy consumption-based efficient offloading method for computing task of mobile edge computing system

Abstract

The invention discloses a delay and energy consumption-based efficient offloading method for a computing task of a mobile edge computing system. The method comprises the steps of building a macro basestation with multiple users and one deployment mobile edge computing (MEC) server and a single-cell cell model of a small base station without an MEC server; then building a mathematical model basedon a wireless communication theory; and finally, designing a delay and energy consumption-based efficient offloading solution for a computing task of the MEC system through a parallel greedy algorithm, and analyzing and providing the performance of the solution. Compared with existing efficient energy consumption offloading solution, the method well weights the delay sensitive demand and energy consumption demand of a terminal user, and lowers delay and total energy consumption of the system.

Description

technical field [0001] The invention belongs to the related fields of mobile communication and mobile edge computing research, and relates to an efficient offloading method for computing tasks of a mobile edge computing system that jointly considers delay and energy consumption, and reduces the delay and energy consumption of the mobile edge computing system. Background technique [0002] In recent years, mobile smart terminals have been widely popularized, and emerging mobile applications such as interactive gaming (IG), virtual reality (virtual reality, VR), augmented reality (augmented reality, AR), etc. are also increasingly popular. However, these mobile applications The program has the characteristics of low latency requirements and high energy consumption, which conflicts with the limited computing power and limited battery capacity and life of the mobile terminal. In this regard, the concept of mobile cloud computing (MCC) for mobile terminals is proposed to solve th...

AI Technical Summary

Problems solved by technology

This algorithm mobile terminal needs the feedback from the MEC server for writing decision, but this algorithm does not consider the signaling overhead generated

The low-complexity Lyapunov optimization-based dynamic computation offloading (LODCO) algorithm uses dynamic voltage scaling (DVS) and energy harvesting techniques (EHT) to optimize Latency consumption, however this algorithm does not take into account the energy consumption of the mobile terminal

The energy-efficient computing offloading mechanism (EECO) classifies mobile terminals to obtain a task offloading solution that optimizes energy consumption. However, this mechanism does not take into account task offloading and execution delays.

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