Multi-user fine-grained task offloading scheduling strategy under cloud-edge-end collaboration

XIE Mande; HUANG Zhufang; SUN Hao

doi:10.11959/j.issn.1000-0801.2024086

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Multi-user fine-grained task offloading scheduling strategy under cloud-edge-end collaboration

Research and Development | 更新时间：2024-06-05

- Multi-user fine-grained task offloading scheduling strategy under cloud-edge-end collaboration
- Telecommunications Science Vol. 40, Issue 4, Pages: 107-121(2024)
- 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：
- 基金信息：
  
  The National Natural Science Foundation of China(61972352);the Key Research and Development Program of Zhejiang Province(2024C01025;2023C01220);the Scientific Research Fund of Zhejiang Provincial Education Department(Y202353408)
- DOI：10.11959/j.issn.1000-0801.2024086
  CLC： TP393
- Received：21 November 2023，
  
  Revised：21 February 2024，
  
  Published：20 April 2024
- 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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