Task Segmentation and Path Optimization in Heterogeneous Cloud-Edge-End Computing Power Network

MA Bo; YU Ying-jie; WU Sha-chen; NI Chang; LU Qin; CHEN Chao; LI Chuan-huang

doi:10.12263/DZXB.20241050

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Task Segmentation and Path Optimization in Heterogeneous Cloud-Edge-End Computing Power Network

Large-Scale Models and the Internet | 更新时间：2025-10-16

- Task Segmentation and Path Optimization in Heterogeneous Cloud-Edge-End Computing Power Network
- ACTA ELECTRONICA SINICA Vol. 53, Issue 6, Pages: 1847-1864(2025)
- 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：
- 基金信息：
  
  National Science Foundation of China(62401506;62301488;62302446);Zhejiang Provincial Science and Technology Innovation Key Project(2023R5211);Natural Science Foundation of Zhejiang Province(LZ23F010003;LQ23F010009);Fundamental Research Funds for the Provincial Universities of Zhejiang(QRK23009)
- DOI：10.12263/DZXB.20241050
  CLC： TP393;
- Received：21 November 2024，
  
  Revised：2025-05-06，
  
  Published：25 June 2025
- 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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