绿色算力网络中智能匹配任务卸载方案

兰世战; 马丽芳

doi:10.11959/j.issn.1000-0801.2025030

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绿色算力网络中智能匹配任务卸载方案

研究与开发 | 更新时间：2025-03-11

- 绿色算力网络中智能匹配任务卸载方案
- Intelligent matching task offloading scheme in green computing power networks
- 电信科学 2025年41卷第2期页码：30-40
- 作者机构：
  
  1.华南理工大学软件学院，广东广州 510006
  2.中国移动通信集团广西有限公司，广西南宁 530012
  3.广西建设职业技术学院信息工程学院，广西南宁 530007
- 作者简介：
  
  [ "兰世战（1981- ），男，中国移动通信集团广西有限公司正高级工程师，主要研究方向为算力网络、移动边缘计算CDN和AI等。" ]
  [ "马丽芳（1980- ），女，广西建设职业技术学院信息工程学院副教授，主要研究方向为计算机应用、云计算和人工智能。" ]
- 基金信息：
  
  国家自然科学基金面上项目(62471208)
- DOI：10.11959/j.issn.1000-0801.2025030
  中图分类号： TP18
- 收稿：2024-09-23，
  
  修回：2024-12-26，
  
  纸质出版：2025-02-20
- 稿件说明：
移动端阅览
兰世战,马丽芳.绿色算力网络中智能匹配任务卸载方案[J].电信科学,2025,41(02):30-40.

LAN Shizhan,MA Lifang.Intelligent matching task offloading scheme in green computing power networks[J].Telecommunications Science,2025,41(02):30-40.
兰世战,马丽芳.绿色算力网络中智能匹配任务卸载方案[J].电信科学,2025,41(02):30-40. DOI： 10.11959/j.issn.1000-0801.2025030.

LAN Shizhan,MA Lifang.Intelligent matching task offloading scheme in green computing power networks[J].Telecommunications Science,2025,41(02):30-40. DOI： 10.11959/j.issn.1000-0801.2025030.

摘要

算力网络通过整合云、边、端算力资源，为数字经济提供数据感知、传输、运算等一体化服务，但其高速发展伴随亟待解决的高能耗问题。任务卸载技术通过合理分配计算任务，提升用户体验、降低传输时延并减少能耗，是一种重要的解决方案。为降低算力网络的整体能耗，实现绿色可持续发展，提出了一种基于匹配机制的智能匹配任务卸载方案。通过匹配算力网络中的任务与节点资源，减少因不合理任务卸载产生的能源消耗，提升算力网络的整体性能表现。同时采用结合强化学习与神经网络的深度学习方法，进一步优化卸载策略，显著降低算力网络能耗。仿真实验验证表明，该方法具有良好的有效性与可靠性。

Abstract

By integrating cloud

edge

and device resources

the computing power networks provide integrated services such as data sensing

transmission

and computation for the digital economy. However

their rapid development is accompanied by pressing challenges of high energy consumption. The task offloading technology is an important solution that allocates computing tasks properly

improves user experience

reduces transmission latency and energy consumption

making it a crucial solution. In order to reduce the overall energy consumption of computing power networks and achieve green

sustainable development

an intelligent matching task unloading scheme based on matching mechanism was proposed. By matching tasks and node resources in the computing power network

the scheme minimized energy consumption caused by inefficient task offloading and enhances overall network performance. Furthermore

a deep learning approach combining reinforcement learning with neural networks was employed to further optimize the offloading strategy

significantly reducing network energy consumption. Simulation experiments demonstrate that the proposed method is effective and reliable.

关键词

Keywords

references

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