基于移动边缘计算的多流自适应卸载方案

胡叠丽; 杨哲铭; 纪雯

doi:10.11959/j.issn.1000-0801.2024233

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基于移动边缘计算的多流自适应卸载方案

研究与开发 | 更新时间：2025-03-17

- 基于移动边缘计算的多流自适应卸载方案
- Multi-stream adaptive offloading scheme based on mobile edge computing
- 电信科学 2024年40卷第11期页码：1-15
- 作者机构：
  
  1.中国科学院计算技术研究所，北京 100190
  2.鹏城实验室，广东深圳 518055
  3.中国科学院大学计算机科学与技术学院，北京 101408
- 作者简介：
  
  [ "胡叠丽（1995- ），女，中国科学院计算技术研究所、鹏城实验室和中国科学院大学计算机科学与技术学院博士生，主要研究方向为边缘计算、多媒体系统、网络优化等。" ]
  [ "杨哲铭（1996- ），男，中国科学院计算技术研究所和中国科学院大学计算机科学与技术学院博士生，主要研究方向为多媒体系统、边缘智能、分布式推理、机器学习等。" ]
  [ "纪雯（1976- ），女，博士，中国科学院计算技术研究所研究员、博士生导师，鹏城实验室兼职教授，主要研究方向为多媒体系统、智能多媒体计算编码传输、多媒体VPU芯片设计等。" ]
- 基金信息：
  
  国家重点研发计划项目(2023YFB4502805);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(L221004)
- DOI：10.11959/j.issn.1000-0801.2024233
  中图分类号： TP393
- 收稿日期：2024-07-30，
  
  修回日期：2024-10-14，
  
  纸质出版日期：2024-11-20
- 稿件说明：
移动端阅览
胡叠丽,杨哲铭,纪雯.基于移动边缘计算的多流自适应卸载方案[J].电信科学,2024,40(11):1-15.

HU Dieli,YANG Zheming,JI Wen.Multi-stream adaptive offloading scheme based on mobile edge computing[J].Telecommunications Science,2024,40(11):1-15.
胡叠丽,杨哲铭,纪雯.基于移动边缘计算的多流自适应卸载方案[J].电信科学,2024,40(11):1-15. DOI： 10.11959/j.issn.1000-0801.2024233.

HU Dieli,YANG Zheming,JI Wen.Multi-stream adaptive offloading scheme based on mobile edge computing[J].Telecommunications Science,2024,40(11):1-15. DOI： 10.11959/j.issn.1000-0801.2024233.

摘要

海量视频流的传输和分析需要大量的带宽和计算资源，这对当前基于移动边缘计算（mobile edge computing，MEC）的卸载方案提出了严峻挑战。对此，提出了一种基于多流协同优化框架的自适应卸载方案。首先，在满足长期MEC能量预算的约束条件下，通过联合优化数据流选择决策、服务器卸载决策、带宽资源分配和计算资源分配来最小化视频任务的处理成本。然后，基于李雅普诺夫优化方法，将长期优化问题转化为每个时隙独立的确定性子问题，并利用马尔可夫近似和KKT条件求解每个时隙的混合整数非线性规划问题。仿真结果表明，所提方案在满足长期MEC能量约束的同时，其成本性能显著优于已有基准研究方案。

Abstract

The transmission and analysis of massive video streams require significant edge bandwidth and computational resources

posing severe challenges to the current multimedia frameworks based on mobile edge computing (MEC). To address this issue

an adaptive offloading scheme based on a multi-stream collaborative optimization framework was proposed. Firstly

under the constraint of the long-term MEC energy budget

the processing cost of video tasks was minimized by optimizing the data stream selection decisions

server offloading decisions

bandwidth resource allocation

and computing resource allocation. Then

based on the Lyapunov optimization method

the long-term optimization problem was transformed into independent deterministic subproblems for each time slot

and the mixed-integer nonlinear programming problems for each time slot were solved by Markov approximation and KKT conditions. Simulation results indicate that the proposed scheme not only meets the long-term MEC energy constraint

but also significantly outperforms existing benchmark schemes in terms of cost performance.

关键词

Keywords

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