算力网络中依赖约束任务的分布式协同分配算法

张满钧; 王颖; 喻鹏; 邱雪松; 郭少勇

doi:10.11959/j.issn.1000-436x.TXXB250697

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算力网络中依赖约束任务的分布式协同分配算法

学术论文 | 更新时间：2026-07-16

- 算力网络中依赖约束任务的分布式协同分配算法
- Distributed collaborative allocation algorithm of dependency-constrained tasks in computing power networks
- 通信学报 2026年47卷第6期页码：1-16
- 作者机构：
  
  北京邮电大学网络与交换技术全国重点实验室，北京 100876
- 作者简介：
  
  张满钧（1999- ），女，北京邮电大学博士生，主要研究方向为算力网络、资源调度。
  王颖（1976- ），女，博士，北京邮电大学副教授、博士生导师，主要研究方向为网络管理与通信软件、软件化网络、算力网络、确定性网络等。
  喻鹏（1986- ），男，博士，北京邮电大学副教授、博士生导师，主要研究方向为5G/6G网络智能管控。
  邱雪松（1973- ），男，博士，北京邮电大学教授、博士生导师，主要研究方向为网络与业务管理、物联网与区块链。
  郭少勇（1985- ），男，博士，北京邮电大学教授，主要研究方向为物联网与区块链。
- 基金信息：
  
  智能电网国家科技重大专项（2030）(2025ZD0804700)
- DOI：10.11959/j.issn.1000-436x.TXXB250697
  中图分类号： TP309
- 收稿：2026-01-04，
  
  修回：2026-03-26，
  
  录用：2026-03-26，
  
  纸质出版：2026-06-20
- 稿件说明：
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张满钧,王颖,喻鹏等.算力网络中依赖约束任务的分布式协同分配算法[J].通信学报,2026,47(06):1-16.

Zhang Manjun,Wang Ying,Yu Peng,et al.Distributed collaborative allocation algorithm of dependency-constrained tasks in computing power networks[J].Journal on Communications,2026,47(06):1-16.
张满钧,王颖,喻鹏等.算力网络中依赖约束任务的分布式协同分配算法[J].通信学报,2026,47(06):1-16. DOI： 10.11959/j.issn.1000-436x.TXXB250697.

Zhang Manjun,Wang Ying,Yu Peng,et al.Distributed collaborative allocation algorithm of dependency-constrained tasks in computing power networks[J].Journal on Communications,2026,47(06):1-16. DOI： 10.11959/j.issn.1000-436x.TXXB250697.

摘要

算力网络中任务分配面临任务依赖复杂、资源多维异构且分布不均，以及大规模节点难以全局协同的三重挑战。传统的单一智能体算法难以应对广域环境的动态资源协同，且往往依赖全局状态信息，现有的多智能体强化学习算法又缺乏对任务依赖和网络拓扑的针对性建模，导致全局协作效率低下。针对上述问题，提出了算力网络中依赖约束任务的分布式协同分配算法，先通过层索引计算、三角依赖解耦与资源阈值约束合并处理复杂的子任务依赖，再采用关系图注意力网络（RGAT）表征多维资源关系与拓扑特征，结合多智能体软演员-评论家（MASAC）算法实现分布式优化。实验结果表明，在多个场景下，所提算法相较于基线算法，在任务完成时间和能效方面均有显著提升。

Abstract

Task allocation in computing power networks faces challenges such as complex task dependencies

multi-dimensional and heterogeneous resources with uneven distribution

and difficulty in global collaboration among large-scale nodes. Traditional single agent algorithms were difficult to cope with the dynamic resource collaboration issues in wide area environments

and often relied on global state information

while existing multi-agent reinforcement learning algorithms lacked targeted modeling of task dependencies and network topology

resulting in low global collaboration efficiency. To address these issues

a distributed collaborative allocation algorithms of dependency-constrained tasks in computing power networks was proposed. First

complex subtask dependencies were processed through layering

triangular dependency decoupling and multi-resource threshold merging. Then

relation-graph attention network (RGAT) was adopted to encode multi-resource relations and topology features

combined with multi-agent soft actor-critic (MASAC) for distributed optimization. Experimental results show that in multiple scenarios

the proposed algorithms significantly optimizes task completion time and energy efficiency compared to baseline algorithms.

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references

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