A task scheduling mechanism for heterogeneous multi-network edge computing power networks

PAN Sanming; WANG Yiran; YAN Yaqi; QI Baojin; RAN Pei; WEI Hua; DONG Yuchi

doi:10.11959/j.issn.1000-0801.DXKX260145

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A task scheduling mechanism for heterogeneous multi-network edge computing power networks

更新时间：2026-06-24

- A task scheduling mechanism for heterogeneous multi-network edge computing power networks
- Telecommunications Science (2026)
- 作者机构：
  
  1.中国铁塔股份有限公司，北京 100089
  2.北京邮电大学网络与交换国家重点实验室，北京 100876
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(62401079)
- DOI：10.11959/j.issn.1000-0801.DXKX260145
  CLC： TP393
- Received：04 March 2026，
  
  Revised：2026-03-27，
  
  Accepted：01 June 2026，
- 稿件说明：
移动端阅览
PAN Sanming, WANG Yiran, YAN Yaqi, et al. A task scheduling mechanism for heterogeneous multi-network edge computing power networks[J/OL]. Telecommunications Science, 2026.
DOI：

PAN Sanming, WANG Yiran, YAN Yaqi, et al. A task scheduling mechanism for heterogeneous multi-network edge computing power networks[J/OL]. Telecommunications Science, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260145.

摘要

针对异构异网边缘算力网络中节点硬件形态差异和多网络接入并存所导致的计算与通信资源强耦合、调度复杂度高等问题，提出了一种任务调度机制与算法。构建了面向异构计算资源与多网络域协同管理的层次化边缘算力网络架构，并在此基础上设计了基于近端策略优化的异构感知任务调度算法；通过联合优化计算节点选择与网络路径分配，在满足任务时延约束和可信执行要求的条件下，降低了系统成本与端到端执行时延。仿真结果表明，所提机制在动态边缘环境中能够有效降低任务执行时延和总体成本，综合性能优于传统调度方法。该机制可为异构异网边缘算力网络中的高效任务调度提供有效支撑。

Abstract

To address the strong coupling between computing and communication resources and the high scheduling complexity caused by heterogeneous node hardware and multi-network access in edge computing power networks

a task scheduling mechanism and algorithm were proposed. A hierarchical edge computing power network architecture for the coordinated management of heterogeneous computing resources and multiple network domains was constructed

and a heterogeneity-aware task scheduling algorithm based on proximal policy optimization was developed on this basis. By jointly optimizing computing-node selection and network-path allocation

system cost and end-to-end execution delay were reduced while task latency constraints and trusted execution requirements were satisfied. Simulation results showed that the proposed mechanism effectively reduced task execution delay and overall cost in dynamic edge environments and achieved better overall performance than conventional scheduling methods. The proposed mechanism provides an effective solution for task scheduling in heterogeneous multi-network edge computing power networks.

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references

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