级联图神经网络用于边缘计算环境中的联合根本原因定位与分析
A Cascaded Graph Neural Network for Joint Root Cause Localization and Analysis in Edge Computing Environments
AI总结：
本文提出级联图神经网络框架，用于边缘计算环境中的联合根本原因定位与分析，解决扩展性和延迟问题，实现高效故障诊断。
摘要：
边缘计算环境正日益复杂，托管着基于微服务的物联网应用，这些应用容易出现性能异常在依赖服务之间传播。识别故障组件（根本原因定位）和底层故障类型（根本原因分析）对于及时缓解至关重要。监督图神经网络（GNNs）目前是联合根本原因定位和分析的最先进方法。然而，现有方法依赖于在完整系统图上进行集中处理，导致推理延迟高且在大规模分布式边缘环境中扩展性有限。在本文中，我们提出了一种级联GNN框架，用于联合RCL和故障类型识别，明确解决了这些扩展性挑战。我们的方法采用通信驱动的聚类将大型服务图划分为高度交互的社区，并采用具有两个子网络的级联网络进行分层RCL/RCA。通过将信息传递限制在减少和结构化的子图中，所提出的框架显著降低了计算复杂性，同时保留了关键依赖信息。我们评估了所提出的方法在MicroCERCL基准和使用iAnomaly模拟框架生成的大规模数据集上的表现。实验结果表明，级联架构在诊断准确率上与集中GNN基线相当，同时在图规模增加时保持几乎恒定的推理延迟，从而在边缘计算环境中实现了可扩展且可操作的AIOps。
英文摘要：
Edge computing environments host increasingly complex microservice-based IoT applications that are prone to performance anomalies propagating across dependent services. Identifying the faulty component (root cause localization) and the underlying fault type (root cause analysis) is essential for timely mitigation. Supervised graph neural networks (GNNs) currently represent the state of the art for joint root cause localization and analysis. However, existing approaches rely on centralized processing over full-system graphs, leading to high inference latency and limited scalability in large, distributed edge environments. In this paper, we propose a cascaded GNN framework for joint RCL and fault type identification that explicitly addresses these scalability challenges. Our approach employs communication-driven clustering to partition large service graphs into highly interacting communities and a cascaded network with two subnetworks that perform hierarchical RCL/RCA. By restricting message passing to reduced and structured subgraphs, the proposed framework significantly lowers computational complexity while preserving critical dependency information. We evaluate the proposed method on the MicroCERCL benchmark and large-scale datasets generated using the iAnomaly simulation framework. Experimental results show that the cascaded architecture achieves diagnostic accuracy comparable to centralized GNN baselines while maintaining near-constant inference latency as graph size increases, enabling scalable and actionable AIOps in edge computing environments.
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