基于GNN与多智能体强化学习的绿色算力网络自愈机制

张建荣

doi:10.11959/j.issn.1000-0801.DXKX260388

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基于GNN与多智能体强化学习的绿色算力网络自愈机制

研究与开发 | 更新时间：2026-09-15

- 基于GNN与多智能体强化学习的绿色算力网络自愈机制
- Self-healing mechanism of green computing power network based on GNN and multi-agent reinforcement learning
- 电信科学 2026年
- 作者机构：
  
  中讯邮电咨询设计院有限公司，北京 100048
- 作者简介：
  
  张建荣，（1990- ），男，硕士研究生，中讯邮电咨询设计院有限公司，工程师，主要研究方向为算力网络、云计算和人工智能等。
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.DXKX260388
  中图分类号： TP393
- 收稿：2026-06-09，
  
  修回：2026-07-27，
  
  录用：2026-08-20，
- 稿件说明：
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张建荣. 基于GNN与多智能体强化学习的绿色算力网络自愈机制[J/OL]. 电信科学, 2026.

ZHANG Jianrong. Self-healing mechanism of green computing power network based on GNN and multi-agent reinforcement learning[J/OL]. Telecommunications Science, 2026.
张建荣. 基于GNN与多智能体强化学习的绿色算力网络自愈机制[J/OL]. 电信科学, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260388.

ZHANG Jianrong. Self-healing mechanism of green computing power network based on GNN and multi-agent reinforcement learning[J/OL]. Telecommunications Science, 2026. DOI： 10.11959/j.issn.1000-0801.DXKX260388.

摘要

绿色算力网络要求故障自愈同时兼顾恢复速度与能耗效率，现有方法多采用集中式控制，缺乏拓扑感知和分布式协作能力，且较少考虑能耗优化。为此，提出了一种基于图神经网络（Graph Neural Network，GNN）与多智能体深度确定性策略梯度（Multi-Agent Deep Deterministic Policy Gradient，MADDPG）的故障自愈机制，该机制在每个子网部署独立智能体，通过GNN编码器融合局部观测与拓扑结构，使智能体具备全局拓扑感知能力，并利用集中式Critic促进多智能体协作决策，实现网络故障自愈。同时，设计了奖励函数联合优化恢复率、恢复时间与能耗。在Fat‑Tree拓扑上的仿真实验验证了该机制自愈性能和能效优化方面的有效性，以及在不同故障密度、动态流量与非线性功耗模型下的鲁棒性。在真实GÉANT拓扑上的泛化实验验证了该机制的可迁移性。

Abstract

Green computing power network requires fault self-healing while taking into account the recovery speed and energy efficiency. The existing methods mostly adopt centralized control

lack of topology awareness and distributed collaboration capabilities

and less consider energy optimization. Therefore

a fault self-healing mechanism based on Graph Neural Network (GNN) and Multi-Agent Deep Deterministic Policy Gradient (MADDPG) is proposed. The mechanism deploys independent agents in each subnet. The local observation and topology structure are fused by GNN encoder

so that the agent has global topology perception ability. The centralized Critic is used to promote multi-agent collaborative decision-making and realize network fault self-healing. At the same time

a reward function is designed to jointly optimize the recovery rate

recovery time and energy consumption. Simulation experiments on the Fat-Tree topology verify the effectiveness of the self-healing performance and energy efficiency optimization of the mechanism

as well as the robustness under different fault density

dynamic traffic and nonlinear power consumption models. The generalization experiment on the real GÉANT topology verifies the mobility of the mechanism.

关键词

Keywords

references

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