基于次模优化的边云协同多用户计算任务迁移方法

梁冰; 纪雯

doi:10.11959/j.issn.1000-436x.2020205

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基于次模优化的边云协同多用户计算任务迁移方法

专题：面向万物互联的通信与计算融合 | 更新时间：2024-06-05

- 基于次模优化的边云协同多用户计算任务迁移方法
- Multiuser computation offloading for edge-cloud collaboration using submodular optimization
- 通信学报 2020年41卷第10期页码：25-36
- 作者机构：
  
  1. 中国科学院计算技术研究所，北京 100190
  2. 中国科学院大学计算机科学与技术学院，北京 100190
  3. 移动计算与新型终端北京市重点实验室，北京 100190
  4. 鹏城实验室，广东深圳 518055
- 作者简介：
  
  [ "梁冰（1992- ），男，河北保定人，中国科学院计算技术研究所博士生，主要研究方向为多媒体通信与网络、视频传输、边缘计算、机器学习等" ]
  [ "纪雯（1976- ），女，陕西西安人，博士，中国科学院计算技术研究所研究员、博士生导师，主要研究方向为多媒体通信与网络，包括视频传输和编码、优化理论和信息论、边缘计算、多媒体经济学和智能计算等" ]
- 基金信息：
  
  国家重点研发计划基金资助项目(2017YFB1400100);国家自然科学基金资助项目(62072440);北京市自然科学基金资助项目(4202072)
- DOI：10.11959/j.issn.1000-436x.2020205
  中图分类号： TN92
- 网络首发：2020-10，
  
  纸质出版：2020-10-25
- 稿件说明：
移动端阅览
梁冰, 纪雯. 基于次模优化的边云协同多用户计算任务迁移方法[J]. 通信学报, 2020,41(10):25-36.

Bing LIANG, Wen JI. Multiuser computation offloading for edge-cloud collaboration using submodular optimization[J]. Journal on Communications, 2020, 41(10): 25-36.
梁冰, 纪雯. 基于次模优化的边云协同多用户计算任务迁移方法[J]. 通信学报, 2020,41(10):25-36. DOI： 10.11959/j.issn.1000-436x.2020205.

Bing LIANG, Wen JI. Multiuser computation offloading for edge-cloud collaboration using submodular optimization[J]. Journal on Communications, 2020, 41(10): 25-36. DOI： 10.11959/j.issn.1000-436x.2020205.

摘要

为了提升多用户计算任务卸载时的系统效用，提出了一种基于边云联合计算的多用户任务卸载方案。该方案在提升系统效用的同时，考虑了边云资源的协同优化问题。针对计算任务卸载模式的选择及边云资源分配的问题，设计了一种基于次模理论的贪心算法并充分利用了云端以及边缘端的计算和通信资源。仿真结果表明，所提方案能够有效降低计算任务执行的时延和能耗，且当多用户卸载计算任务时，所提方案在资源受限的条件下仍然能够保持稳定的系统性能。

Abstract

A computation offloading scheme based on edge-cloud computing was proposed to improve the system utility of multiuser computation offloading.This scheme improved the system utility while considering the optimization of edge-cloud resources.In order to tackle the problems of computation offloading mode selection and edge-cloud resource allocation

a greedy algorithm based on submodular theory was developed by fully exploiting the computing and communication resources of cloud and edge.The simulation results demonstrate that the proposed scheme effectively reduces the delay and energy consumption of computing tasks.Additionally

when computing tasks are offloaded to edge and cloud from devices

the proposed scheme still maintains stable system utilities under ultra-limited resources.

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references

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