6G网络任务卸载与细粒度切片资源调度联合优化算法

王晔; 王逸飞; 陈康; 朱晓荣; 童恩; 徐语菲

doi:10.11959/j.issn.1000-0801.2024144

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6G网络任务卸载与细粒度切片资源调度联合优化算法

研究与开发 | 更新时间：2024-08-05

- 6G网络任务卸载与细粒度切片资源调度联合优化算法
- Joint optimization algorithm for 6G network task offloading and fine-grained slice resource scheduling
- 电信科学 2024年40卷第5期页码：86-99
- 作者机构：
  
  1.江苏移动信息系统集成有限公司，江苏南京 210013
  2.南京邮电大学通信与信息工程学院，江苏南京 210003
  3.中国移动通信集团江苏有限公司，江苏南京 210013
- 作者简介：
  
  [ "王晔（1987- ），男，博士，江苏移动信息系统集成有限公司技术总监、工程师，主要研究方向为移动蜂窝技术、车路协同、算力网络、工业互联网。" ]
  [ "王逸飞（2000- ），男，南京邮电大学硕士生，主要研究方向为网络切片、资源调度等。" ]
  [ "陈康（1998- ），男，南京邮电大学硕士生，主要研究方向为无线通信、接入网切片技术等。" ]
  [ "朱晓荣（1977- ），女，南京邮电大学教授、博士生导师，主要研究方向为5G/6G通信系统、物联网、区块链等。" ]
  [ "童恩（1971- ），男，博士，中国移动通信集团江苏有限公司首席专家、正高级工程师，主要研究方向为移动蜂窝技术、物联网、车联网、大数据等。" ]
  [ "徐语菲（1997- ），女，现就职于江苏移动信息系统集成有限公司，主要研究方向为工业互联网。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61871237;92067101);江苏省高校“青蓝工程”基金资助项目;江苏省高校“青蓝工程”和江苏省重点研发计划项目;Program to Cultivate Middle-aged and Young Science Leaders of Universities of Jiangsu Province and Key Research and Development Plan of Jiangsu Province(BE2021013-3);江苏省研究生科研与实践创新计划基金资助项目(KYCX21_0733)
- DOI：10.11959/j.issn.1000-0801.2024144
  中图分类号： TN92
- 收稿日期：2023-11-26，
  
  修回日期：2024-04-20，
  
  纸质出版日期：2024-05-20
- 稿件说明：
移动端阅览
王晔,王逸飞,陈康等.6G网络任务卸载与细粒度切片资源调度联合优化算法[J].电信科学,2024,40(05):86-99.

WANG Ye,WANG Yifei,CHEN Kang,et al.Joint optimization algorithm for 6G network task offloading and fine-grained slice resource scheduling[J].Telecommunications Science,2024,40(05):86-99.
王晔,王逸飞,陈康等.6G网络任务卸载与细粒度切片资源调度联合优化算法[J].电信科学,2024,40(05):86-99. DOI： 10.11959/j.issn.1000-0801.2024144.

WANG Ye,WANG Yifei,CHEN Kang,et al.Joint optimization algorithm for 6G network task offloading and fine-grained slice resource scheduling[J].Telecommunications Science,2024,40(05):86-99. DOI： 10.11959/j.issn.1000-0801.2024144.

摘要

针对未来全域、全场景多样化的业务需求，6G网络需要提供场景化、个性化的服务能力。针对未来细粒度业务服务质量保障问题，提出了6G网络任务卸载与细粒度切片资源调度联合优化算法，联合考虑多MEC的计算卸载和网络切片的资源调度，在有限的资源内，最小化任务的执行时延和能耗成本，并采用异步训练的A3C强化学习算法进行求解。仿真结果表明，对比传统算法，该算法可以在满足用户业务需求的情况下降低计算成本，并且算法收敛速度快，可以实现快速决策。

Abstract

In response to the diverse business needs of the future in all domains and scenarios

6G networks need to provide scenario-based and personalized service capabilities. Aiming at the problem of quality assurance of fine-grained business services in the future

a joint optimization algorithm for 6G network task offloading and fine-grained slice resource scheduling was proposed

which jointly considered the calculation offloading of multiple MECs and the resource scheduling of network slices

and minimized the execution delay and energy consumption cost of the task within limited resources. Then the A3C reinforcement learning algorithm of asynchronous training was used to solve it. The simulation results show that

compared with the traditional algorithm

the proposed algorithm can reduce the computing cost while meeting the business needs of users. Additionally

the algorithm converges fast and can realize fast decision-making.

关键词

Keywords

references

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YANG Y J , CHEN X , CHEN Y , et al . Green-oriented offloading and resource allocation by reinforcement learning in MEC [C ] // Proceedings of the 2019 IEEE International Conference on Smart Internet of Things (SmartIoT) . Piscataway : IEEE Press , 2019 : 378 - 382 .

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