云边端异构算力网络计算任务分割与路径优化方法研究

马博; 余应洁; 吴莎尘; 倪畅; 陆琴; 陈超; 李传煌

doi:10.12263/DZXB.20241050

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云边端异构算力网络计算任务分割与路径优化方法研究

大模型与互联网 | 更新时间：2025-10-16

- 云边端异构算力网络计算任务分割与路径优化方法研究
- Task Segmentation and Path Optimization in Heterogeneous Cloud-Edge-End Computing Power Network
- 电子学报 2025年53卷第6期页码：1847-1864
- 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：
  
  [ "马博男，1991年9月出生于宁夏回族自治区.现为浙江工商大学信息与电子工程学院副教授、硕士生导师.主要研究方向为异构网优化、无人机通信、5G网络中的机器学习算法.E-mail: mabo@mail.zjgsu.edu.cn" ]
  [ "余应洁男，2001年7月出生于浙江省.现为浙江工商大学信息与电子工程学院硕士研究生.主要研究方向为算力网络.E-mail: 19858874414@163.com" ]
  [ "吴莎尘女，2000年7月出生于云南省.现为浙江工商大学信息与电子工程学院硕士研究生.主要研究方向为算力网络、任务调度.E-mail: wshachen@163.com" ]
  [ "倪畅男，2001年1月出生于浙江省.现为浙江工商大学信息与电子工程学院（萨塞克斯人工智能学院）硕士研究生.主要研究方向为无人机通信优化问题、算力网络优化问题. E-mail: cn385@sussex.ac.uk" ]
  [ "陆琴女，1999年2月出生于安徽省.现为浙江工商大学信息与电子工程学院硕士研究生.主要研究方向为图神经网络、深度强化学习、算力网络等.E-mail: 21020090013@pop.zjgsu.edu.cn" ]
  [ "陈超男，1986年12月出生于浙江省.现为浙江工商大学信息与电子工程学院副教授、硕士生导师.主要研究方向为无线通信网络技术、网络编码、机器/深度学习.E-mail: eckio_491@zjgsu.edu.cn" ]
  [ "李传煌男，1980年8月出生于江西省，现为浙江工商大学信息与电子工程学院教授、硕士生导师.主要研究方向为新一代网络技术、算力网络、人工智能应用.E-mail: chuanhuang_li@zjgsu.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(62401506;62301488;62302446);浙江省科技创新重点项目(2023R5211);浙江省自然科学基金(LZ23F010003;LQ23F010009);浙江省属高校基本业务费专项资金资助项目(QRK23009)
- DOI：10.12263/DZXB.20241050
  中图分类号： TP393;
- 收稿：2024-11-21，
  
  修回：2025-05-06，
  
  纸质出版：2025-06-25
- 稿件说明：
移动端阅览
马博, 余应洁, 吴莎尘, 等. 云边端异构算力网络计算任务分割与路径优化方法研究[J]. 电子学报, 2025, 53(06): 1847-1864.

MA Bo, YU Ying-jie, WU Sha-chen, et al. Task Segmentation and Path Optimization in Heterogeneous Cloud-Edge-End Computing Power Network[J]. Acta Electronica Sinica, 2025, 53(06): 1847-1864.
马博, 余应洁, 吴莎尘, 等. 云边端异构算力网络计算任务分割与路径优化方法研究[J]. 电子学报, 2025, 53(06): 1847-1864. DOI：10.12263/DZXB.20241050

MA Bo, YU Ying-jie, WU Sha-chen, et al. Task Segmentation and Path Optimization in Heterogeneous Cloud-Edge-End Computing Power Network[J]. Acta Electronica Sinica, 2025, 53(06): 1847-1864. DOI：10.12263/DZXB.20241050

摘要

在云边端算力网络中，传输、计算和存储资源的协同优化是一个关键且极具挑战性的课题.如何有效融合高性能的云资源、低时延的边缘资源、广泛分布的节点资源以及低成本的用户资源，实现智能化的资源分发、关联、交易与调配，对于整网资源的最优化配置和高效利用意义重大.本文针对传输与计算融合的云边端异构算力网络，构建了详细的数学模型.从算力需求、资源分发、交易与调配等多个维度出发，将异构计算与传输资源调度中的时延和成本最小化联合优化问题，转化为混合整数非线性规划问题.随后，本文提出了一种创新的串行子任务路径分配机制，并结合最优路径最大化分配算法（Optimal Route and Assign Maximizer algorithm，ORAM），以实现任务计算与传输路径的高效协同优化.该机制将计算任务分割为多个子任务，感知并处理串联子任务之间的依赖关系，利用ORAM算法实时选择符合依赖关系的最优计算路径，指导计算结果以最少跳数的方式传输至目标节点，形成端到端的高效资源调度通道.这不仅降低了传输时延和资源成本，还将传统的“先传后算”模式有效转变为“传算协同”模式.实验结果显示，在不同的计算需求、感知范围和节点数量条件下，本文所提出的算法相较于多种基准算法，在时延、成本及路径优化等方面均表现出更优的性能.

Abstract

Collaborative optimization of transmission

computation

and storage resources in “cloud-edge-end” computing power networks is a critical and highly challenging task. Effectively integrating high-performance cloud resources

low-latency edge resources

widely distributed node resources

and low-cost user resources to achieve intelligent resource distribution

association

trading

and allocation are essential for the optimal configuration and efficient utilization of network-wide resources. This paper constructs a detailed mathematical model for the “cloud-edge-end” heterogeneous computing power networks with a focus on the integration of transmission and computation. Addressing multiple dimensions such as computing power demand

resource distribution

trading

and allocation

the joint optimization problem of minimizing delay and cost in scheduling heterogeneous computing and transmission resources is transformed into a mixed-integer nonlinear programming problem. Subsequently

an innovative serial sub-task path allocation mechanism is proposed

combined with the optimal route and assignment maximization (ORAM)

to achieve efficient collaborative optimization of task computation and transmission paths. This mechanism divides computing tasks into multiple sub-tasks

perceives and manages the dependencies between serial sub-tasks

and utilizes the ORAM algorithm to select optimal computation paths that satisfy dependency relationships in real-time. It directs the transmission of computation results to target nodes with the fewest hops

thereby forming an end-to-end efficient resource scheduling channel. This approach not only reduces transmission delay and resource costs but also effectively transforms the traditional “transmit-then-compute” model into a “transmit-compute collaborative” model. Experimental results demonstrate that the proposed algorithm outperforms various benchmark algorithms in terms of delay

cost

and path optimization under different computational demands

sensing ranges

and node quantities.

关键词

Keywords

references

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