用图神经网络提升分布式系统多节点协同感知与调度效率
Graph Neural Network-Based Collaborative Perception for Adaptive Scheduling in Distributed Systems
- 构建图结构模型，通过消息传递实现节点间动态状态推理
- 在异构负载与动态拓扑下，任务完成率提升18.7%，延迟降低23.4%
- 适合需要实时响应的云原生、边缘计算系统部署
本文针对分布式系统中多节点感知能力有限和调度响应延迟的问题，提出一种基于图神经网络（GNN）的多节点协同感知机制。系统被建模为图结构，引入消息传递与状态更新模块，构建多层图神经网络以实现节点间高效信息聚合与动态状态推断。同时设计融合局部状态与全局特征的感知表示方法，增强各节点对整体系统状态的感知能力。在自定义实验框架下进行评估，使用包含异构任务负载和动态通信拓扑的数据集，性能指标包括任务完成率、平均延迟、负载均衡度和传输效率。实验结果表明，该方法在带宽受限及结构动态变化等多种条件下均优于主流算法，展现出优异的感知能力和协同调度表现，模型具备快速收敛与对复杂系统状态的高效响应能力。
原文摘要 · Abstract (English)
This paper addresses the limitations of multi-node perception and delayed scheduling response in distributed systems by proposing a GNN-based multi-node collaborative perception mechanism. The system is modeled as a graph structure. Message-passing and state-update modules are introduced. A multi-layer graph neural network is constructed to enable efficient information aggregation and dynamic state inference among nodes. In addition, a perception representation method is designed by fusing local states with global features. This improves each node's ability to perceive the overall system status. The proposed method is evaluated within a customized experimental framework. A dataset featuring heterogeneous task loads and dynamic communication topologies is used. Performance is measured in terms of task completion rate, average latency, load balancing, and transmission efficiency. Experimental results show that the proposed method outperforms mainstream algorithms under various conditions, including limited bandwidth and dynamic structural changes. It demonstrates superior perception capabilities and cooperative scheduling performance. The model achieves rapid convergence and efficient responses to complex system states.
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