移动边缘计算环境下基于移动感知的工作流调度算法

doi:10.16180/j.cnki.issn1007-7820.2022.09.009

Abstract

Abstract:

In the mobile edge computing environment, users can offload local computing-intensive tasks to the edge server, thereby shortening the completion time of the workflow and saving equipment energy consumption. However, many studies have neglected the influence of network connection changes caused by user movement on workflow scheduling. In view of the unreasonable unloading problem in the existing algorithms, a workflow scheduling algorithm MaWS is proposed. The algorithm predicts the user's movement trajectory to obtain a set of future communicable base stations, and incorporates genetic algorithms to formulate reasonable task execution sequence and execution position. The simulation results show that compared with algorithms such as HEFT and Greedy, the MaWS algorithm can effectively shorten the completion time of the workflow by 10% to 15% and reduce the energy consumption of the equipment by 8% to 13%, which indicates that the proposed MaWS algorithm is an effective solution for workflow scheduling under mobile edge computing.

Key words: mobile edge computing, workflow scheduling, mobile perception, task offloading, genetic algorithm, energy-saving, trajectory prediction, network connection changes

CLC Number:

TP393

WEI Zefeng,ZHOU Yuanyuan. Workflow Scheduling Algorithm Based on Mobile Perception in Mobile Edge Environment[J].Electronic Science and Technology, 2022, 35(9): 58-64.

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Workflow Scheduling Algorithm Based on Mobile Perception in Mobile Edge Environment

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