云边端协同下多用户细粒度任务卸载调度策略

谢满德; 黄竹芳; 孙浩

doi:10.11959/j.issn.1000-0801.2024086

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云边端协同下多用户细粒度任务卸载调度策略

研究与开发 | 更新时间：2024-06-05

- 云边端协同下多用户细粒度任务卸载调度策略
- Multi-user fine-grained task offloading scheduling strategy under cloud-edge-end collaboration
- 电信科学 2024年40卷第4期页码：107-121
- 作者机构：
  
  浙江工商大学信息与电子工程学院，浙江杭州 310018
- 作者简介：
  
  [ "谢满德（1977- ），男，博士，浙江工商大学信息与电子工程学院（萨塞克斯人工智能学院）副院长、博士生导师，主要研究方向为边缘计算、云计算、无线传感器网络和网络安全。" ]
  [ "黄竹芳（1999- ），女，浙江工商大学信息与电子工程学院（萨塞克斯人工智能学院）硕士生，主要研究方向为边缘计算。" ]
  [ "孙浩（1993- ），男，博士，浙江工商大学信息与电子工程学院（萨塞克斯人工智能学院）讲师，主要研究方向为边缘计算、边缘智能。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61972352);浙江省重点研发计划项目(2024C01025;2023C01220);浙江省教育厅科研项目(Y202353408)
- DOI：10.11959/j.issn.1000-0801.2024086
  中图分类号： TP393
- 收稿日期：2023-11-21，
  
  修回日期：2024-02-21，
  
  纸质出版日期：2024-04-20
- 稿件说明：
移动端阅览
谢满德,黄竹芳,孙浩.云边端协同下多用户细粒度任务卸载调度策略[J].电信科学,2024,40(04):107-121.

XIE Mande,HUANG Zhufang,SUN Hao.Multi-user fine-grained task offloading scheduling strategy under cloud-edge-end collaboration[J].Telecommunications Science,2024,40(04):107-121.
谢满德,黄竹芳,孙浩.云边端协同下多用户细粒度任务卸载调度策略[J].电信科学,2024,40(04):107-121. DOI： 10.11959/j.issn.1000-0801.2024086.

XIE Mande,HUANG Zhufang,SUN Hao.Multi-user fine-grained task offloading scheduling strategy under cloud-edge-end collaboration[J].Telecommunications Science,2024,40(04):107-121. DOI： 10.11959/j.issn.1000-0801.2024086.

摘要

为了解决当前处理多用户应用程序效率低下、密集网络资源利用率低及系统花费成本高等问题，提出了一种云边端协同下多用户细粒度任务卸载调度方法。该方法联合考虑了时延、能耗和服务器租用成本，先划分应用程序任务并设计子任务优先级，然后提出了多用户子任务调度方案，设计了一种改进的模拟退火粒子群算法求解最小系统总成本，从而实现最佳卸载决策。实验结果表明，所提方法相较于粒子群和模拟退火二元粒子群等其他方法，分别降低了至少12.28%和7.42%的总成本。

Abstract

To solve the current problems of inefficiency

low utilization of intensive network resources

and high system cost in handling multi-user applications

a multi-user fine-grained task offloading scheduling approach under cloud-edge-end collaboration was proposed. Latency

energy consumption

and server rental costs were jointly considered. Application tasks were firstly divided and subtask priorities were designed. Then

a multi-user subtask scheduling scheme was proposed and an improved simulated annealing particle swarm algorithm was designed to minimize the total system cost to achieve the optimal offloading decision. Experimental results show that the proposed method reduces the total cost by at least 12.28% and 7.42% compared to other methods such as particle swarm and simulated annealing binary particle swarm

respectively.

关键词

Keywords

references

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