Distributed collaborative allocation algorithm of dependency-constrained tasks in computing power networks

Zhang Manjun; Wang Ying; Yu Peng; Qiu Xuesong; Guo Shaoyong

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

您当前的位置：

首页 >

文章列表页 >

Distributed collaborative allocation algorithm of dependency-constrained tasks in computing power networks

Papers | 更新时间：2026-07-16

- Distributed collaborative allocation algorithm of dependency-constrained tasks in computing power networks
- Journal on Communications Vol. 47, Issue 6, Pages: 1-16(2026)
- 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：
- 基金信息：
  
  Smart Grid-National Science and Technology (2030) Major Project(2025ZD0804700)
- DOI：10.11959/j.issn.1000-436x.TXXB250697
  CLC： TP309
- Received：04 January 2026，
  
  Revised：2026-03-26，
  
  Accepted：26 March 2026，
  
  Published：20 June 2026
- 稿件说明：
移动端阅览
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.

关键词

Keywords

references

Mao Y Y , You C S , Zhang J , et al . A survey on mobile edge computing: the communication perspective [J ] . IEEE Communications Surveys Tutorials , 2017 , 19 ( 4 ): 2322 - 2358 .

贾庆民 , 胡玉姣 , 张华宇 , 等 . 确定性算力网络研究 [J ] . 通信学报 , 2022 , 43 ( 10 ): 55 - 64 .

Jia Q M , Hu Y J , Zhang H Y , et al . Research on deterministic computing power network [J ] . Journal on Communications , 2022 , 43 ( 10 ): 55 - 64 .

中国联通 . 中国联通算力网络白皮书 [R ] . 2019 .

China Unicom . China Unicom computing power network white paper [R ] . 2019 .

Zou W H , Zhang Z S , Wang N N , et al . ReflexPilot: startup-aware dependent task scheduling based on deep reinforcement learning for edge-cloud collaborative computing [J ] . IEEE Transactions on Cloud Computing , 2025 , 13 ( 2 ): 641 - 654 .

Fan Q , Ansari N . Application aware workload allocation for edge computing-based IoT [J ] . IEEE Internet of Things Journal , 2018 , 5 ( 3 ): 2146 - 2153 .

Pompili D , Hajisami A , Viswanathan H . Dynamic provisioning and allocation in cloud radio access networks (C-RANs) [J ] . Ad Hoc Networks , 2015 , 30 : 128 - 143 .

Kipf T N , Welling M . Semi-supervised classification with graph convolutional networks [PP ] . V4 . ( 2017-02-22 )[ 2026-01-04 ] . arXiv: arXiv. 1609 . 02907 .

Veličković P , Cucurull G , Casanova A , et al . Graph attention networks [PP ] . V3 . ( 2018-02-04 )[ 2026-01-04 ] . arXiv: arXiv. 1710 . 10903 .

Tian X , Meng Y . Relgraph: a multi-relational graph neural network framework for knowledge graph reasoning based on relation graph [J ] . Applied Sciences , 2024 , 14 ( 7 ): 3122 .

Jiang F B , Dong L , Wang K Z , et al . Distributed resource scheduling for large-scale MEC systems: a multiagent ensemble deep reinforcement learning with imitation acceleration [J ] . IEEE Internet of Things Journal , 2022 , 9 ( 9 ): 6597 - 6610 .

Lu H F , Gu C H , Luo F , et al . Optimization of lightweight task offloading strategy for mobile edge computing based on deep reinforcement learning [J ] . Future Generation Computer Systems , 2020 , 102 : 847 - 861 .

Lowe R , Wu Y , Tamar A , et al . Multi-agent actor-critic for mixed cooperative-competitive environments [PP ] . V4 . ( 2020-03-14 )[ 2026-01-04 ] . arXiv: arXiv. 1706 . 02275 .

Chen Z Y , Zhang S C , Tang Y , et al . Optimization of computing power network routing strategies based on multi-agent soft actor-critic [C ] // Proceedings of the 2024 9th International Conference on Intelligent Computing and Signal Processing (ICSP) . Piscataway : IEEE Press , 2024 : 426 - 430 .

Zhang J W , Chen S H , Wang X D , et al . Dynamic reservation of edge servers via deep reinforcement learning for connected vehicles [J ] . IEEE Transactions on Mobile Computing , 2023 , 22 ( 5 ): 2661 - 2674 .

Yu C M , Ku M L , Wang L C , et al . BRATRA: balanced routing algorithm with transmission range adjustment for energy efficiency and utilization balance in WSNs [J ] . IEEE Internet of Things Journal , 2023 , 10 ( 2 ): 1096 - 1111 .

Chen Y , Xu J J , Wu Y , et al . Dynamic task offloading and resource allocation for NOMA-aided mobile edge computing: an energy efficient design [J ] . IEEE Transactions on Services Computing , 2024 , 17 ( 4 ): 1492 - 1503 .

Liao H L , Li X Y , Guo D K , et al . Dependency-aware application assigning and scheduling in edge computing [J ] . IEEE Internet of Things Journal , 2022 , 9 ( 6 ): 4451 - 4463 .

Sohaib M , Jeon S W , Yu W . Hybrid online-offline learning for task offloading in mobile edge computing systems [J ] . IEEE Transactions on Wireless Communications , 2024 , 23 ( 7 ): 6873 - 6888 .

Shi J M , Du J , Wang J J , et al . Priority-aware task offloading in vehicular fog computing based on deep reinforcement learning [J ] . IEEE Transactions on Vehicular Technology , 2020 , 69 ( 12 ): 16067 - 16081 .

Tang Q Q , Xie R C , Yu F R , et al . Collective deep reinforcement learning for intelligence sharing in the Internet of intelligence-empowered edge computing [J ] . IEEE Transactions on Mobile Computing , 2023 , 22 ( 11 ): 6327 - 6342 .

Huang X Y , He L J , Chen X , et al . Revenue and energy efficiency-driven delay-constrained computing task offloading and resource allocation in a vehicular edge computing network: a deep reinforcement learning approach [J ] . IEEE Internet of Things Journal , 2022 , 9 ( 11 ): 8852 - 8868 .

Tang Q Q , Xie R C , Yu F R , et al . Decentralized computation offloading in IoT fog computing system with energy harvesting: a dec-POMDP approach [J ] . IEEE Internet of Things Journal , 2020 , 7 ( 6 ): 4898 - 4911 .

Gao A , Zhang S , Hu Y S , et al . Game-combined multi-agent DRL for tasks offloading in wireless powered MEC networks [J ] . IEEE Transactions on Vehicular Technology , 2023 , 72 ( 7 ): 9131 - 9144 .

Liu Q Q , Zhang H X , Zhang X , et al . Joint service caching, communication and computing resource allocation in collaborative MEC systems: a DRL-based two-timescale approach [J ] . IEEE Transactions on Wireless Communications , 2024 , 23 ( 10 ): 15493 - 15506 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Congestion control for RDMA with end-network collaboration in computing power network

Federated learning with differential privacy recalibration for dynamic computing nodes

Service-generated networking: architectures, technologies, and prospects

Internet of computing architecture: next generation network architecture for supporting cross-region computing resources interconnection based on entropy balance

Research on computing power network technology for stateless computing services

Related Author

Liu Yaping

Yan Dingyu

Fang Binxing

Xu Mingguang

Zhang Shuo

Yang Zhikai

CHEN Ningjiang

ZHENG Zezhang

Related Institution

Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Ministry of Education

Pengcheng Laboratory (PCL)

Cyberspace Institute of Advanced Technology, Guangzhou University

Key Laboratory of Parallel, Distributed and Intelligent Computing(Guangxi University), Education Department of Guangxi Zhuang Autonomous Region

Guangxi Center of Technology Innovation for Intelligent Digital Services

AI问答

⁰