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

电子科技 ›› 2022, Vol. 35 ›› Issue (9): 58-64.doi: 10.16180/j.cnki.issn1007-7820.2022.09.009

移动边缘计算环境下基于移动感知的工作流调度算法

魏泽丰¹,周元元²

1.杭州电子科技大学计算机学院,浙江杭州 310018
2.合肥师范学院电子信息系统仿真设计安徽省重点实验室,安徽合肥 23009

收稿日期:2021-03-29 出版日期:2022-09-15 发布日期:2022-09-15
作者简介:魏泽丰(1995-),男,硕士研究生。研究方向:移动边缘计算。|周元元(1983-),男,副教授。研究方向:智能信号处理。
基金资助:
浙江省重点研发项目(2018C01012);合肥师范学院省级科研平台专项项目(2020PTZD07)

Workflow Scheduling Algorithm Based on Mobile Perception in Mobile Edge Environment

WEI Zefeng¹,ZHOU Yuanyuan²

1. Computer and Software School,Hangzhou Dianzi University,Hangzhou 310018,China
2. Anhui Province Key Laboratory of Simulation and Design for Electronic Information System,Hefei Normal University,Hefei 230039,China

Received:2021-03-29 Online:2022-09-15 Published:2022-09-15
Supported by:
Key R&D Program of Zhejiang(2018C01012);Special Project of Provincial Scientific Research Platform of Hefei Normal University(2020PTZD07)

摘要/Abstract

摘要：

移动边缘计算环境下,用户可将本地的计算密集型任务卸载至边缘服务器,从而缩短工作流的完工时间并节省设备能耗。然而,许多研究忽略了用户移动导致的网络连接变化对工作流调度的影响。针对现有算法中存在的卸载不合理问题,文中提出了基于移动感知的工作流调度算法MaWS。该算法通过预测用户移动轨迹得出未来可通信的基站集合,并融入遗传算法,制定合理的任务执行顺序和执行位置。仿真结果表明,相比HEFT和Greedy等算法,MaWS算法能够有效缩短10%~15%的工作流完工时间并降低8%~13%的设备能耗,为移动边缘计算下的工作流调度提出一种有效方案。

关键词: 移动边缘计算, 工作流调度, 移动感知, 任务卸载, 遗传算法, 节约能耗, 轨迹预测, 网络连接变化

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

中图分类号:

TP393

魏泽丰,周元元. 移动边缘计算环境下基于移动感知的工作流调度算法[J]. 电子科技, 2022, 35(9): 58-64.

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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移动边缘计算环境下基于移动感知的工作流调度算法

Workflow Scheduling Algorithm Based on Mobile Perception in Mobile Edge Environment

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